Past and future spread of the arbovirus vectors



Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictusKraemer, M.U.G.1,2,3,$, Reiner Jr., R.C.4,$, Brady, O.J.5,$, Messina, J.P.1,$, Gilbert, M.6,7,$, Pigott, D.M.4, Yi, D.8, Johnson, K.4, Earl, L.4, Marczak, L.B.4, Shirude, S.4, Davis Weaver, N.4, Bisanzio, D.9, Perkins, T.A.10, Lai, S.11,12,13, Lu, X.12,13,14,15, Jones, P.16, Coelho, G.E.17, Carvalho, R.G.17, Van Bortel, W.18,19, Marsboom, C.20, Hendrickx, G.20, Schaffner, F.21, Moore, C.G.22, Nax, H.H.23, Bengtsson, L.13,15, Wetter, E.13,24, Tatem, A.J.12,13,, Brownstein, J.S.2,3, Smith, D.L.4,25, Lambrechts, L.26,27, Cauchemez, S.28,29, Linard, C.6, 30, Faria, N.R.1, Pybus, O.G.1, Scott, T.W.31, Liu, Q.32,33,34,35, Yu, H.11, Wint, G.R.W.1,36, Hay, S.I.4,9§, Golding, N.37§1Department of Zoology, University of Oxford, Oxford, UK2Harvard Medical School, Harvard University, Boston, USA3Boston Children’s Hospital, Boston, USA4Institute for Health Metrics and Evaluation, University of Washington, WA 98121, USA5Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK6Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, B-1050 Brussels, Belgium7Fonds National de la Recherche Scientifique, B-1000 Brussels, Belgium8Department of Statistics, Harvard University, Cambridge MA, USA9Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK10Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, USA11School of Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China12Department of Geography and Environment, University of Southampton, Southampton, UK13Flowminder Foundation, Stockholm, Sweden14College of Information System and Management, National University of Defense Technology, Changsha, China15Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden16Waen Associates Ltd, Y Waen, Islaw’r Dref, Dolgellau, Gwynedd LL401TS, UK17National Dengue Control Program, Ministry of Health, Brasilia, DF, Brazil18European Centre for Disease Prevention and Control, Stockholm, Sweden19Institute of Tropical Medicine, Antwerp, Belgium20Avia-GIS, Zoersel, Belgium21Francis Schaffner Consultancy, Riehen, Switzerland22Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA23Computational Social Science, ETH Zurich, Zurich, Switzerland24Stockholm School of Economics, Stockholm, Sweden25Sanaria Institute for Global Health and Tropical Medicine, Rockville, USA26Insect-Virus Interactions Group, Department of Genomes and Genetics, Institut Pasteur, Paris 75015, France27Centre National de la Recherche Scientifique, Unité de Recherche Associée 3012, Paris 75015, France28Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France29Centre National de la Recherche Scientifique, URA3012, Paris, France30Department of Geography, Universite de Namur, Belgium31Department of Entomology and Nematology, University of California, Davis, USA32State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing 102206, China33Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China34WHO Collaborating Centre for Vector Surveillance and Management, 155 Changbai Road, Changping, Beijing 102206, China35Centre for Environment and Population Health, Nathan Campus, Griffith University, 170 Kessels Road, Queensland 4111, Nathan, QLD, Australia36Environmental Research Group Oxford (ERGO), Department of Zoology, Oxford University, Oxford, UK37School of BioSciences, University of Melbourne, VIC 3010, AustraliaCorrespondence to:Moritz U G Kraemer, DPhilDepartment of Zoology, University of OxfordOxford, OX13SP, United KingdomEmail: moritz.kraemer@zoo.ox.ac.ukNick Golding, DPhilSchool of BioSciences, University of MelbourneVIC 3010, Australianick.golding.research@ Prof. Simon I Hay, DScInstitute for Health Metrics and EvaluationUniversity of Washington,WA 98121, United Statessihay@uw.edu$ Contributed equally to this work as first authors§These authors jointly supervised this workOne Sentence SummaryHuman mobility patterns and climate changes predict the spread of the arbovirus vectors Aedes aegypti and Ae. albopictus, which transmit viruses such as dengue, yellow fever, chikungunya, and Zika.AbstractThe global population at risk from mosquito-borne diseases – including dengue, yellow fever, chikungunya, and Zika – is expanding in concert with changes in the distribution of two key vectors, Aedes aegypti and Ae. albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States of America (USA) following their introduction. We find that the spread of Ae. aegypti is characterised by long distance importations, whilst Ae. albopictus has expanded more along the fringes of its current distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanisation, connectivity, and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programs and thereby augment efforts to reduce arbovirus burden in human populations globally.Main textThe geographical distributions of the arboviruses dengue, yellow fever, chikungunya, and Zika have expanded, causing severe disease outbreaks in many urban populations.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Nsoesie","given":"E O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"M U","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"D M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"O J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moyes","given":"C L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johansson","given":"M A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"P W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Velayudhan","given":"R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Khan","given":"K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bronwstein","given":"J.S","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Eurosurveillance","id":"ITEM-1","issue":"20","issued":{"date-parts":[["2015"]]},"page":"pii=30234","title":"Global distribution and environmental suitability for chikungunya virus, 1952 to 2015","type":"article-journal","volume":"21"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"","author":[{"dropping-particle":"","family":"Messina","given":"Jane P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"Moritz U.G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shearer","given":"Freya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weiss","given":"D.J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ruktanonchai","given":"C.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Khan","given":"K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"A.J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jaenisch","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Murray","given":"C.J.L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marinho","given":"F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"T.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"eLife","id":"ITEM-2","issued":{"date-parts":[["2016"]]},"page":"e15272","title":"Mapping global environmental suitability for Zika virus","type":"article-journal","volume":"5"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1038/nature12060","ISSN":"1476-4687","PMID":"23563266","abstract":"Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes. 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Brazil has the highest number of reported ZIKV cases worldwide (>200,000 by 24 Dec 2016) as well as the greatest number of cases associated with microcephaly and other birth defects (2,366 confirmed cases by 31 Dec 2016). Following the initial detection of ZIKV in Brazil, 47 countries and territories in the Americas have reported local ZIKV transmission, with 24 of these reporting ZIKV-associated severe disease. Yet the origin and epidemic history of ZIKV in Brazil and the Americas remain poorly understood, despite the value of such information for interpreting past and future trends in reported microcephaly. To address this we generated 54 complete or partial ZIKV genomes, mostly from Brazil, and report data generated by the ZiBRA project - a mobile genomics lab that travelled across Northeast (NE) Brazil in 2016. One sequence represents the earliest confirmed ZIKV infection in Brazil. Joint analyses of viral genomes with ecological and epidemiological data estimate that ZIKV epidemic was present in NE Brazil by March 2014 and likely disseminated from there, both nationally and internationally, before the first detection of ZIKV in the Americas. Estimated dates of the international spread of ZIKV from Brazil indicate the duration of pre-detection cryptic transmission in recipient regions. 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Previous predictions of the future distributions of Aedes aegypti [=Stegomyia aegypti] and Ae. albopictus [=Stegomyia albopicta] have focussed solely on climate, despite the known importance of urbanisation and other socioeconomic factors in defining suitable habitatADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Messina","given":"Jane P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"Moritz U.G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"T.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wint","given":"G.R.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"D.L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Reviews Microbiology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2015"]]},"page":"230-9","title":"The many projected futures of dengue.","type":"article-journal","volume":"13"},"uris":[""]}],"mendeley":{"formattedCitation":"⁸","plainTextFormattedCitation":"8","previouslyFormattedCitation":"⁸"},"properties":{"noteIndex":0},"schema":""}8. 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Recent trends in the global spread of these species, however, suggest that the process of expansion may be more complex and spatially structured than previously acknowledgedADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Powell","given":"Jeffrey R","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Science","id":"ITEM-1","issue":"6315","issued":{"date-parts":[["2016"]]},"page":"971-972","title":"Mosquitoes on the move","type":"article-journal","volume":"354"},"uris":["",""]}],"mendeley":{"formattedCitation":"¹⁰","plainTextFormattedCitation":"10","previouslyFormattedCitation":"¹⁰"},"properties":{"noteIndex":0},"schema":""}10. Expansion from the native ranges in Ae. aegypti (from African forests) and Ae. albopictus (from Asia) was precipitated by a shift from zoophily to anthropophily and by adaptation to container-breeding in domestic or peri-domestic environmentsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1590/0074-0276130395","ISSN":"1678-8060","PMID":"24473798","abstract":"The adaptation of insect vectors of human diseases to breed in human habitats (domestication) is one of the most important phenomena in medical entomology. Considerable data are available on the vector mosquito Aedes aegypti in this regard and here we integrate the available information including genetics, behaviour, morphology, ecology and biogeography of the mosquito, with human history. We emphasise the tremendous amount of variation possessed by Ae. aegypti for virtually all traits considered. 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Whilst their short flight ranges limit self-powered dispersalADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1756-3305-7-276","ISBN":"1756-3305","ISSN":"1756-3305","PMID":"24946878","abstract":"BACKGROUND: Pathogen transmission by mosquitos is known to be highly sensitive to mosquito bionomic parameters. Mosquito mark-release-recapture (MMRR) experiments are a standard method for estimating such parameters including dispersal, population size and density, survival, blood feeding frequency and blood meal host preferences.$\\$n$\\$nMETHODS: We assembled a comprehensive database describing adult female MMRR experiments. Bibliographic searches were used to build a digital library of MMRR studies and selected data describing the reported outcomes were extracted.$\\$n$\\$nRESULTS: The resulting database contained 774 unique adult female MMRR experiments involving 58 vector mosquito species from the three main genera of importance to human health: Aedes, Anopheles and Culex. Crude examination of these data revealed patterns associated with geography as well as mosquito genus, consistent with bionomics varying by species-specific life history and ecological context. Recapture success varied considerably and was significantly different amongst genera, with 8, 4 and 1{%} of adult females recaptured for Aedes, Anopheles and Culex species, respectively. A large proportion of experiments (59{%}) investigated dispersal and survival and many allowed disaggregation of the release and recapture data. 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A century of rapid human population growth and international trade has enabled their global spread. 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It is of medical importance due to its aggressive daytime human-biting behavior and ability to vector many viruses, including dengue, LaCrosse, and West Nile. Invasions into new areas of its potential range are often initiated through the transportation of eggs via the international trade in used tires. We use a genetic algorithm, Genetic Algorithm for Rule Set Production (GARP), to determine the ecological niche of Ae. albopictus and predict a global ecological risk map for the continued spread of the species. We combine this analysis with risk due to importation of tires from infested countries and their proximity to countries that have already been invaded to develop a list of countries most at risk for future introductions and establishments. 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Considerable data are available on the vector mosquito Aedes aegypti in this regard and here we integrate the available information including genetics, behaviour, morphology, ecology and biogeography of the mosquito, with human history. We emphasise the tremendous amount of variation possessed by Ae. aegypti for virtually all traits considered. Typological thinking needs to be abandoned to reach a realistic and comprehensive understanding of this important vector of yellow fever, dengue and Chikungunya.","author":[{"dropping-particle":"","family":"Powell","given":"Jeffrey R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tabachnick","given":"Walter J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Memórias do Instituto Oswaldo Cruz","id":"ITEM-2","issued":{"date-parts":[["2013","1"]]},"page":"11-7","title":"History of domestication and spread of Aedes aegypti - a review.","type":"article-journal","volume":"108 Suppl"},"uris":[""]}],"mendeley":{"formattedCitation":"^11,19","plainTextFormattedCitation":"11,19","previouslyFormattedCitation":"^11,19"},"properties":{"noteIndex":0},"schema":""}11,19, the processes underlying intra-continental spread of the species remain poorly quantified, preventing informed prediction of future distributions. Modelling of human-mediated range expansion suggests that quantitative models of human movement could, and should, be used to predict intra-continental spreadADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Padilla","given":"D.K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chotkowski","given":"M.A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buchan","given":"L.A.J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Global Ecology and Biogeography Letters","id":"ITEM-1","issue":"6","issued":{"date-parts":[["1996"]]},"page":"353-359","title":"Predicting the Spread of Zebra Mussels (Dreissena polymorpha) to Inland Waters Using Boater Movement Patterns","type":"article-journal","volume":"5"},"uris":["",""]},{"id":"ITEM-2","itemData":{"DOI":"10.1371/journal.pone.0125600","ISSN":"1932-6203","author":[{"dropping-particle":"","family":"Roche","given":"Benjamin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Léger","given":"Lucas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"L’Ambert","given":"Grégory","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lacour","given":"Guillaume","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Foussadier","given":"Rémi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Besnard","given":"Gilles","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barré-Cardi","given":"Hélène","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Simard","given":"Frédéric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fontenille","given":"Didier","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Plos One","id":"ITEM-2","issue":"5","issued":{"date-parts":[["2015"]]},"page":"e0125600","title":"The Spread of Aedes albopictus in Metropolitan France: Contribution of Environmental Drivers and Human Activities and Predictions for a Near Future","type":"article-journal","volume":"10"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.4018/jagr.2013040102","author":[{"dropping-particle":"","family":"Moore","given":"Chester G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Medicine","given":"Veterinary","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sciences","given":"Biomedical","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"State","given":"Colorado","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Applied Geospatial Research","id":"ITEM-3","issued":{"date-parts":[["2013"]]},"page":"9-38","title":"Using Geographic Information Systems to Analyze the Distribution and Abundance of Aedes aegypti in Africa : The Potential Role of Human Travel in Determining the Intensity of Mosquito Infestation","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"^20–22","plainTextFormattedCitation":"20–22","previouslyFormattedCitation":"^20–22"},"properties":{"noteIndex":0},"schema":""}20–22. To address this, we developed predictive models of Ae. aegypti and Ae. albopictus spread and combined these with forecasts of future climatic conditions and urban growth, to predict the ranges of these medically important vectors from 2015 to 2080 (Extended Data Fig. 1).We collated spatially- and temporally-explicit data on the distributions of Ae. aegypti and Ae. albopictus and their spread over time in the USA, and Ae. albopictus in Europe (Fig. 1, Extended Data Figs. 2, 3). Extending a previous studyADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Kraemer","given":"M. U. 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We then parameterised quantitative models of human mobility using census data on migration and commuting patternsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1003716","ISSN":"1553-7358","author":[{"dropping-particle":"","family":"Tizzoni","given":"Michele","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bajardi","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Decuyper","given":"Adeline","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kon Kam King","given":"Guillaume","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schneider","given":"Christian M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blondel","given":"Vincent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smoreda","given":"Zbigniew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Colizza","given":"Vittoria","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS Computational Biology","editor":[{"dropping-particle":"","family":"Salathé","given":"Marcel","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issue":"7","issued":{"date-parts":[["2014","7","10"]]},"page":"e1003716","title":"On the use of human mobility proxies for modeling epidemics","type":"article-journal","volume":"10"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/nature10856","ISSN":"1476-4687","PMID":"22367540","abstract":"Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. 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Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.","author":[{"dropping-particle":"","family":"Simini","given":"Filippo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maritan","given":"Amos","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barabási","given":"Albert-László","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-2","issue":"7392","issued":{"date-parts":[["2012","4","5"]]},"page":"96-100","title":"A universal model for mobility and migration patterns.","type":"article-journal","volume":"484"},"uris":[""]}],"mendeley":{"formattedCitation":"^23,24","plainTextFormattedCitation":"23,24","previouslyFormattedCitation":"^23,24"},"properties":{"noteIndex":0},"schema":""}23,24, and general movement patterns derived from mobile phone logs (call detail records) (Extended Data Fig. 1)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1003716","ISSN":"1553-7358","author":[{"dropping-particle":"","family":"Tizzoni","given":"Michele","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bajardi","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Decuyper","given":"Adeline","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kon Kam King","given":"Guillaume","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schneider","given":"Christian M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blondel","given":"Vincent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smoreda","given":"Zbigniew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Colizza","given":"Vittoria","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS Computational Biology","editor":[{"dropping-particle":"","family":"Salathé","given":"Marcel","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issue":"7","issued":{"date-parts":[["2014","7","10"]]},"page":"e1003716","title":"On the use of human mobility proxies for modeling epidemics","type":"article-journal","volume":"10"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/nature10856","ISSN":"1476-4687","PMID":"22367540","abstract":"Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. 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Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.","author":[{"dropping-particle":"","family":"Simini","given":"Filippo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maritan","given":"Amos","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barabási","given":"Albert-László","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-2","issue":"7392","issued":{"date-parts":[["2012","4","5"]]},"page":"96-100","title":"A universal model for mobility and migration patterns.","type":"article-journal","volume":"484"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1371/journal.pone.0060069","ISSN":"1932-6203","PMID":"23555885","abstract":"Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to any arbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxes extracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape.","author":[{"dropping-particle":"","family":"Simini","given":"Filippo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maritan","given":"Amos","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Néda","given":"Zoltán","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PloS one","id":"ITEM-3","issue":"3","issued":{"date-parts":[["2013","1"]]},"page":"e60069","title":"Human mobility in a continuum approach.","type":"article-journal","volume":"8"},"uris":[""]}],"mendeley":{"formattedCitation":"^23–25","plainTextFormattedCitation":"23–25","previouslyFormattedCitation":"^23–25"},"properties":{"noteIndex":0},"schema":""}23–25. The combined predictions from these different mobility models and datasets capture different aspects of human travel and trade, and their ability to spread Aedes eggs and juveniles at different spatial scales.We tabulated annualised presence records which documented the first detection of each species in 1,567 different locations over 38 years in Europe (225 / 1,588 districts, between 1979 - 2016) and 32 years in the USA (1,342 / 3,134 counties, between 1985 and 2016) (Extended Data Fig. 2a, b, c). These data were used to parameterise statistical models of spatial spread for each species. Detection within a given area was modelled as a function of i) the receptivity of the area (as determined by the habitat suitability models), ii) long-distance importation pressure (from multiple human movement models) and iii) short-distance importation pressure from adjacent areas (to represent natural dispersal). Forward simulation of these fitted models of spatial spread was then used to predict the future spread or recession of each species, considering climate changes, urbanisation, and human-mediated importation. To account for potentially biased sampling procedures we performed a comprehensive sensitivity analysis assuming different levels of detection for both species (Supplementary Information).Short-range importation between adjacent districts played a greater role in the inferred spread process for Ae. albopictus (Fig. 1a, c, d, f) than for Ae. aegypti (Fig. 1b, e), which was more frequently imported over longer distances. Historically, most of the observed range expansion of Ae. aegypti in the USA originated from southern States (Fig. 1b, Extended Data Fig. 2b). Using thin plate spline regression, we estimated the localised invasion velocity of Ae. aegypti spread in the USA to be relatively homogeneous at ~250km per year (Fig. 1b, e). Aedes albopictus spread in the USA was fastest between 1990 and 1995 (Fig. 1a, d) and has since slowed to about ~60km per year. In contrast, the estimated rate of spread of Ae. albopictus in Europe is faster (~100km per year) rising to ~150km per year over the last five years (Fig. 1c, f, Extended Data Fig. 2c, f, i). The geographic origin of recent Ae. albopictus spread in Europe seems to be Italy, with the Alps serving as a dispersal barrier that lowers rates of spread (Extended Data Fig. 2c, f). Once that barrier has been overcome, however, spread rates beyond the Alps are as high as in Italy. This may explain the increased rate of spread in recent years, which also corresponds to the detection of Ae. albopictus in areas north of the Alps (Extended Data Fig. 2c, f).Using human-mobility-driven statistical models we can predict the past spread of both mosquito species with high reliability (Extended Data Fig. 6) and accuracy (out of sample area under the receiver operating characteristic curve [AUC]: 0.7-0.9, Extended Data Fig. 7). Only slight improvements are observed when including human mobility models over models that only included distance and adjacency metrics (Supplementary Information, Extended Data Fig. 12). Further, we evaluated our models’ ability to predict the range expansion in Europe using a model fitted to US data (1,149 records) only. This test similarly documented a high degree of predictive ability (out of sample AUC: 0.8-0.9, Extended Data Fig. 8). In addition, country borders seem not to limit the spread of the mosquitoes (Extended Data Fig. 11) and our spread model is robust even under different assumptions in mosquito sampling strategies but the underlying observational data may impact our estimates of velocity of spread (Supplementary Information). In contrast, the model fitted to only European data was unable to predict the spread in the USA, presumably because of the relatively few Ae. albopictus records in Europe compared to the USA (192 records). Therefore we used the model fitted to USA data to project the range of both species into the future (Supplementary Information). Both Ae. aegypti and Ae. albopictus are anticipated to continue expanding beyond their current distributions (Extended Data Figs. 4, 5). For Ae. aegypti, predicted future spread is mostly concentrated within its tropical range and in new temperate areas in the USA and China; reaching as far north as Chicago and Shanghai by 2050 (Figs. 2, 4, Extended Data Fig. 4). At the expansion front in the United States, our model predicts the spread to occur mostly through long-distance introductions in large urban areas (Figs. 2a, b, Extended Data Fig. 10). Even under the most extreme scenarios (RCP8.5 in 2080), Ae. aegypti is predicted to establish in Europe in only a few isolated regions of southern Italy and Turkey (Extended Data Fig. 4). By 2080 we predict there will be 159 countries worldwide (range 156 – 162) reporting this species, of which three (range 0-6) will be reporting it for the first time (Extended Data Tab. 8).By contrast, Ae. albopictus is expected to spread broadly through Europe, ultimately reaching wide areas of France and Germany (Fig. 3b). Areas in northern USA and highland regions of South America and East Africa are also projected to see establishment of Ae. albopictus over the next 30 years (Figs. 2, 4). At the same time, some areas are predicted to become less suitable for the species, particularly locations in central southern USA (Fig. 2, Extended Data Fig. 5) and Eastern Europe (Fig. 3) where climate models indicate aridity will increase. Due to Ae. albopictus broader distribution in northern latitudes, as in the USA, the spread pressure follows a clear front-like expansion (Figs. 2c, d). In total, 197 countries (range 181-209) are expected to report Ae. albopictus by 2080, 20 (range 4-32) of those countries will be reporting its presence for the first time (Extended Data Tab. 8).Spread of both species over the next 5-15 years is predicted to occur independently of extensive environmental changes as both species continue to expand into their anthropogenic ecological niches through spatial dispersal. Aedes albopictus is anticipated to saturate its ecological niche between 2030 and 2050 (Figs. 4d, f), and Ae. aegypti by 2020 (Figs. 4a,c). Beyond these dates the predicted expansion of these species will be driven primarily by environmental changes that create new habitat, including changes in climate, especially temperature (Extended Data Tab. 1, 2), as well as exploitation of the increased availability of large human urban environments. Thus efforts to curb or reverse climate change are predicted to be insufficient to prevent fully the expansion of these vector species; significantly greater expansion, however, is predicted, especially between 2050 and 2080, if emissions are not reduced (Fig. 4). At the same time, future human population growth is expected to be concentrated disproportionately within areas where Ae. aegypti and Ae. albopictus already will be established, leading to large increases in the global population at risk of diseases transmitted by these species.Overall our predicted expansions will see Ae. aegypti invading an estimated 19.96 million km2 by 2050 (19.91 – 23.45 million km2, depending on the climate and urbanisation scenarios), placing an estimated 49.13% (48.23 – 58.10%) of the world’s population at risk of arbovirus transmission (Figs. 4c, f).Few countries conduct routine, systematic surveillance for Ae. aegypti and Ae. albopictus. Consequently our analysis relies on datasets from the USA and Europe that contain spatio-temporal biases in reporting (Extended Data Fig. 2), with an implicit assumption that the processes driving spread in these regions apply elsewhere. These regions have (i) a comparatively high capacity to track establishment and mitigate the spread of these species and (ii) openly available datasets on human movementADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/IJERPH14040444","ISSN":"1660-4601","abstract":"Aedes albopictus (tiger mosquito) has become the most invasive mosquito species worldwide, in addition to being a well-known vector of diseases, with a proven capacity for the transmission of chikungunya and dengue viruses in Europe as well as the Zika virus in Africa and in laboratory settings. This research quantifies the cost that needs to be provided by public-health systems for area-wide prevention of arboviruses in Europe. This cost has been calculated by evaluating the expenditure of the plan for Aedes albopictus control set up in the Emilia-Romagna region (Northern Italy) after a chikungunya outbreak occurred in 2007. This plan involves more than 280 municipalities with a total of 4.2 million inhabitants. Public expenditure for plan implementation in 2008–2011 was examined through simple descriptive statistics. Annual expenditure was calculated to be approximately €1.3 per inhabitant, with a declining trend (from a total of €7.6 million to €5.3 million) and a significant variability at the municipality level. The preventative measures in the plan included antilarval treatments (about 75% of total expenditure), education for citizens and in schools, entomological surveillance, and emergency actions for suspected viremias. Ecological factors and the relevance of tourism showed a correlation with the territorial variability in expenditure. The median cost of one antilarval treatment in public areas was approximately €0.12 per inhabitant. 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Our modeled rate of spread is thus most likely to be biased towards an underestimate of the global rate of spread (Supplementary Information). We did not model potential changes in human mobility which could increase the rate of spread of both species as population mobility increases. 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Moreover, local outbreaks of these arboviruses have typically followed within 5-15 years of infestation by Ae. aegypti and Ae. albopictus, emphasising the importance of vector spread importation as a key risk factor for arbovirus transmission.There is significant uncertainty surrounding future predictions of changes in climatic conditions. We used an ensemble approach to propagate the uncertainty from climate scenarios through our predictions of both Aedes species (Figs. 2, 3, 4, Extended Data Figs. 4, 5). Even under current climate conditions and population densities, both vector species will continue to spread globally over the coming decades, filling unoccupied suitable habitats and posing a risk to human health in the majority of locations where they survive and reproduce. Thus efforts to prevent their global dissemination in the near future will be most effective if focussed on preventing human-mediated spread and establishment. To prevent introductions, countries should strengthen entomological surveillance, particularly around high-risk introduction routes such as ports and highways and develop rapid response protocols for vector control to prevent introduced mosquitoes from establishing permanent populationsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1756-3305-6-209","ISBN":"9789291933785","ISSN":"15607917","PMID":"23866915","abstract":"BACKGROUND: The recent notifications of autochthonous cases of dengue and chikungunya in Europe prove that the region is vulnerable to these diseases in areas where known mosquito vectors (Aedes albopictus and Aedes aegypti) are present. Strengthening surveillance of these species as well as other invasive container-breeding aedine mosquito species such as Aedes atropalpus, Aedes japonicus, Aedes koreicus and Aedes triseriatus is therefore required. In order to support and harmonize surveillance activities in Europe, the European Centre for Disease Prevention and Control (ECDC) launched the production of 'Guidelines for the surveillance of invasive mosquitoes in Europe'. This article describes these guidelines in the context of the key issues surrounding invasive mosquitoes surveillance in Europe.\\n\\nMETHODS: Based on an open call for tender, ECDC granted a pan-European expert team to write the guidelines draft. It content is founded on published and grey literature, contractor's expert knowledge, as well as appropriate field missions. Entomologists, public health experts and end users from 17 EU/EEA and neighbouring countries contributed to a reviewing and validation process. The final version of the guidelines was edited by ECDC (Additional file 1).\\n\\nRESULTS: The guidelines describe all procedures to be applied for the surveillance of invasive mosquito species. The first part addresses strategic issues and options to be taken by the stakeholders for the decision-making process, according to the aim and scope of surveillance, its organisation and management. As the strategy to be developed needs to be adapted to the local situation, three likely scenarios are proposed. The second part addresses all operational issues and suggests options for the activities to be implemented, i.e. key procedures for field surveillance of invasive mosquito species, methods of identification of these mosquitoes, key and optional procedures for field collection of population parameters, pathogen screening, and environmental parameters. In addition, methods for data management and analysis are recommended, as well as strategies for data dissemination and mapping. Finally, the third part provides information and support for cost estimates of the planned programmes and for the evaluation of the applied surveillance process.\\n\\nCONCLUSION: The 'Guidelines for the surveillance of invasive mosquitoes in Europe' aim at supporting the implementation of tailored surveillance of i…","author":[{"dropping-particle":"","family":"Schaffner","given":"Francis","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bellini","given":"Romeo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Petri?","given":"Du?an","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scholte","given":"Ernst-Jan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zeller","given":"Hervé","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rakotoarivony","given":"Laurence M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & Vectors","id":"ITEM-1","issued":{"date-parts":[["2013"]]},"page":"209","title":"Development of guidelines for the surveillance of invasive mosquitoes in Europe","type":"article-journal","volume":"6"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.3201/eid0303.970309","ISSN":"1080-6040","PMID":"9284377","abstract":"Since its discovery in Houston, Texas, in 1987, the Asian \"tiger mosquito\" Aedes albopictus has spread to 678 counties in 25 states. This species, which readily colonizes container habitats in the peridomestic environment, was probably introduced into the continental United States in shipments of scrap tires from northern Asia. The early pattern of dispersal followed the interstate highway system, which suggests further dispersal by human activities. The Public Health Service Act of 1988 requires shipments of used tires from countries with Ae. albopictus to be treated to prevent further importations. Given the extensive spread of the mosquito in the United States, it is questionable whether such a requirement is still justified. Ae. albopictus, a major biting pest throughout much of its range, is a competent laboratory vector of at least 22 arboviruses, including many viruses of public health importance. Cache Valley and eastern equine encephalomyelitis viruses are the only human pathogens isolated from U.S. populations of Ae. albopictus. There is no evidence that this mosquito is the vector of human disease in the United States.","author":[{"dropping-particle":"","family":"Moore","given":"C G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mitchell","given":"C J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Emerging infectious diseases","id":"ITEM-2","issue":"3","issued":{"date-parts":[["1997"]]},"page":"329-34","title":"Aedes albopictus in the United States: ten-year presence and public health implications.","type":"article-journal","volume":"3"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1186/s13071-015-0793-6","author":[{"dropping-particle":"","family":"Flacio","given":"Eleonora","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Engeler","given":"Lukas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tonolla","given":"Mauro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lüthy","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Patocchi","given":"Nicola","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites and Vectors","id":"ITEM-3","issue":"208","issued":{"date-parts":[["2015"]]},"title":"Strategies of a thirteen year surveillance programme on Aedes albopictus (Stegomyia albopicta) in southern Switzerland","type":"article-journal","volume":"8"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1186/s13071-015-1262-y","ISSN":"1756-3305","author":[{"dropping-particle":"","family":"Collantes","given":"Francisco","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Delacour","given":"Sarah","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alarcón-elbal","given":"Pedro María","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ruiz-arrondo","given":"Ignacio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Delgado","given":"Juan Antonio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Torrell-sorio","given":"Antonio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bengoa","given":"Mikel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eritja","given":"Roger","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miranda","given":"Miguel ?ngel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Molina","given":"Ricardo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lucientes","given":"Javier","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & Vectors","id":"ITEM-4","issue":"655","issued":{"date-parts":[["2015"]]},"publisher":"Parasites & Vectors","title":"Review of ten-years presence of Aedes albopictu s in Spain 2004 – 2014 : known distribution and public health concerns","type":"article-journal","volume":"8"},"uris":[""]},{"id":"ITEM-5","itemData":{"DOI":"10.1038/s41598-017-12652-5","author":[{"dropping-particle":"","family":"Eritja","given":"Roger","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Palmer","given":"John R B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Roiz","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sanpera-calbet","given":"Isis","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bartumeus","given":"F.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Reports","id":"ITEM-5","issued":{"date-parts":[["2017"]]},"page":"14399","title":"Direct Evidence of Adult Aedes albopictus Dispersal by Car","type":"article-journal","volume":"7"},"uris":[""]}],"mendeley":{"formattedCitation":"^37–41","plainTextFormattedCitation":"37–41","previouslyFormattedCitation":"^37–41"},"properties":{"noteIndex":0},"schema":""}37–41. We expect such efforts will need to intensify over time as human populations become ever more connected and urban agglomerations grow furtherADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.pt.2015.09.006","author":[{"dropping-particle":"","family":"Kraemer","given":"M.U.G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"D.M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"D.L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wint","given":"G.R.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Trends in Parasitology","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2016"]]},"page":"19-29","title":"Progress and challenges in infectious disease cartography","type":"article-journal","volume":"32"},"uris":[""]}],"mendeley":{"formattedCitation":"⁹","plainTextFormattedCitation":"9","previouslyFormattedCitation":"⁹"},"properties":{"noteIndex":0},"schema":""}9.Beyond 2030 and especially 2050, the distributions of both species will continue to expand, co-inciding with niche expansion into climatically suitable urban areas as opposed to the exploration of the current niche. Increased urbanisation worldwide has already put great strains on our ability to prevent the spread of certain disease vectors and has intensified endemic transmission of arbovirusesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Salje","given":"Henrik","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lessler","given":"Justin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berry","given":"Irina Maljkovic","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Melendrez","given":"Melanie C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Endy","given":"Timothy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kalayanarooj","given":"Siripen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"A-nuegoonpipat","given":"Atchareeya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chanama","given":"Sumalee","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sangkijporn","given":"Somchai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klungthong","given":"Chonticha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thaisomboonsuk","given":"Butsaya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nisalak","given":"Ananda","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gibbons","given":"R.V.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Iamsirithaworn","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Macareo","given":"L.R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yoon","given":"I-K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sangarsang","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jarman","given":"R.G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cummings","given":"D.A.T.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Science","id":"ITEM-1","issued":{"date-parts":[["2017"]]},"page":"1302-1306","title":"Dengue diversity across spatial and temporal scales: Local structure and the effect of host population size","type":"article-journal","volume":"355"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴²","plainTextFormattedCitation":"42","previouslyFormattedCitation":"⁴²"},"properties":{"noteIndex":0},"schema":""}42. Some areas may become less suitable for human habitation due to climate change impacts, reducing the number of people living in areas at risk. In the longer term, reducing emission of greenhouse gases would be desirable to limit the increase in Ae. aegypti and Ae. albopictus suitable habitat. Every effort must be made to limit factors that contribute to the global spread of Ae. aegypti and Ae. albopictus if we are to limit the future burden of the diseases vectored by these mosquitoes.MethodsWe used a combination of two approaches to estimate the predicted future distribution of Ae. aegypti and Ae. albopictus: (1) projecting the environmental suitability of both species using a set of seven environmental covariates and (2) simulating the spread within each continent using the species’ past dispersal patterns, human movement data, and between region adjacency matrices (Extended Data Fig. 1). Here we describe the models and data sources for both processes.1. Data1.1. Global mosquito occurrence dataWe used a previously collated database of 19,930 and 22,137 geopositioned occurrence records for Ae. aegypti and Ae. albopictus respectively (Extended Data Fig. 3)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/sdata.2015.35","author":[{"dropping-particle":"","family":"Kraemer","given":"Moritz U G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sinka","given":"Marianne E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duda","given":"Kirsten A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mylne","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shearer","given":"Freya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Messina","given":"Jane P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barker","given":"Christopher M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moore","given":"Chester G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carvalho","given":"Roberta G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Coelho","given":"Giovanini E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"Van","family":"Bortel","given":"Wim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hendrickx","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schaffner","given":"F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wint","given":"G.R.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elyazar","given":"Iqbal R F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Teng","given":"Hwa-Jen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Data","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"page":"150035","title":"The global compendium of Aedes aegypti and Ae . albopictus occurrence","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴³","plainTextFormattedCitation":"43","previouslyFormattedCitation":"⁴³"},"properties":{"noteIndex":0},"schema":""}43. Each of these records corresponds to a unique detection of a mosquito population in a given location at a given point in time, as described in detail elsewhereADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/sdata.2015.35","author":[{"dropping-particle":"","family":"Kraemer","given":"Moritz U G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sinka","given":"Marianne E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duda","given":"Kirsten A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mylne","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shearer","given":"Freya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Messina","given":"Jane P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barker","given":"Christopher M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moore","given":"Chester G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carvalho","given":"Roberta G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Coelho","given":"Giovanini E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"Van","family":"Bortel","given":"Wim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hendrickx","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schaffner","given":"F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wint","given":"G.R.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elyazar","given":"Iqbal R F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Teng","given":"Hwa-Jen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Data","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"page":"150035","title":"The global compendium of Aedes aegypti and Ae . albopictus occurrence","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴³","plainTextFormattedCitation":"43","previouslyFormattedCitation":"⁴³"},"properties":{"noteIndex":0},"schema":""}43. We excluded records that were classified as temporary presence when such information was available.1.2. Environmental and socio-economic covariatesAedes survival is influenced by a variety of climatic and environmental factors such as long term and inter-annual temperatureADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1756-3305-7-338","ISSN":"1756-3305","PMID":"25052008","abstract":"BACKGROUND: Dengue is a disease that has undergone significant expansion over the past hundred years. Understanding what factors limit the distribution of transmission can be used to predict current and future limits to further dengue expansion. While not the only factor, temperature plays an important role in defining these limits. Previous attempts to analyse the effect of temperature on the geographic distribution of dengue have not considered its dynamic intra-annual and diurnal change and its cumulative effects on mosquito and virus populations. METHODS: Here we expand an existing modelling framework with new temperature-based relationships to model an index proportional to the basic reproductive number of the dengue virus. This model framework is combined with high spatial and temporal resolution global temperature data to model the effects of temperature on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. RESULTS: Our model predicted areas where temperature is not expected to permit transmission and/or Aedes persistence throughout the year. By reanalysing existing experimental data our analysis indicates that Ae. albopictus, often considered a minor vector of dengue, has comparable rates of virus dissemination to its primary vector, Ae. aegypti, and when the longer lifespan of Ae. albopictus is considered its competence for dengue virus transmission far exceeds that of Ae. aegypti. CONCLUSIONS: These results can be used to analyse the effects of temperature and other contributing factors on the expansion of dengue or its Aedes vectors. Our finding that Ae. albopictus has a greater capacity for dengue transmission than Ae. aegypti is contrary to current explanations for the comparative rarity of dengue transmission in established Ae. albopictus populations. This suggests that the limited capacity of Ae. albopictus to transmit DENV is more dependent on its ecology than vector competence. The recommendations, which we explicitly outlined here, point to clear targets for entomological investigation.","author":[{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"Moritz U","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Messina","given":"Jane P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reiner","given":"Robert C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"Thomas W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"Simon I","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & vectors","id":"ITEM-1","issued":{"date-parts":[["2014","7","22"]]},"page":"338","title":"Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission.","type":"article-journal","volume":"7"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1186/1756-3305-6-351","ISSN":"1756-3305","PMID":"24330720","abstract":"BACKGROUND: The survival of adult female Aedes mosquitoes is a critical component of their ability to transmit pathogens such as dengue viruses. One of the principal determinants of Aedes survival is temperature, which has been associated with seasonal changes in Aedes populations and limits their geographical distribution. The effects of temperature and other sources of mortality have been studied in the field, often via mark-release-recapture experiments, and under controlled conditions in the laboratory. Survival results differ and reconciling predictions between the two settings has been hindered by variable measurements from different experimental protocols, lack of precision in measuring survival of free-ranging mosquitoes, and uncertainty about the role of age-dependent mortality in the field.\n\nMETHODS: Here we apply generalised additive models to data from 351 published adult Ae. aegypti and Ae. albopictus survival experiments in the laboratory to create survival models for each species across their range of viable temperatures. These models are then adjusted to estimate survival at different temperatures in the field using data from 59 Ae. aegypti and Ae. albopictus field survivorship experiments. The uncertainty at each stage of the modelling process is propagated through to provide confidence intervals around our predictions.\n\nRESULTS: Our results indicate that adult Ae. albopictus has higher survival than Ae. aegypti in the laboratory and field, however, Ae. aegypti can tolerate a wider range of temperatures. A full breakdown of survival by age and temperature is given for both species. The differences between laboratory and field models also give insight into the relative contributions to mortality from temperature, other environmental factors, and senescence and over what ranges these factors can be important.\n\nCONCLUSIONS: Our results support the importance of producing site-specific mosquito survival estimates. By including fluctuating temperature regimes, our models provide insight into seasonal patterns of Ae. aegypti and Ae. albopictus population dynamics that may be relevant to seasonal changes in dengue virus transmission. Our models can be integrated with Aedes and dengue modelling efforts to guide and evaluate vector control, better map the distribution of disease and produce early warning systems for dengue epidemics.","author":[{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johansson","given":"Michael a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guerra","given":"Carlos a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhatt","given":"Samir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Delatte","given":"Hélène","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grech","given":"Marta G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leisnham","given":"Paul T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maciel-de-Freitas","given":"Rafael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Styer","given":"Linda M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"Thomas W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"Simon I","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & vectors","id":"ITEM-2","issued":{"date-parts":[["2013","1"]]},"page":"351","title":"Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings.","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"^44,45","plainTextFormattedCitation":"44,45","previouslyFormattedCitation":"^44,45"},"properties":{"noteIndex":0},"schema":""}44,45, water availability (described as relative humidity and precipitation), and degree of urbanisation. We used projections from the “Representative Concentration Pathways” (RCP) developed by the Intergovernmental Panel on Climate Change (IPCC)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"IPCC","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2013"]]},"publisher":"Cambridge University Press","publisher-place":"New York","title":"Climate Change 2013: the physical science basis: contribution of working group I to the Fifth Assessment Report on The Intergovernmental Panel on Climate Change, IPCC Fifth Assessment Report: Climate Change 2013 (AR5).","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁶","plainTextFormattedCitation":"46","previouslyFormattedCitation":"⁴⁶"},"properties":{"noteIndex":0},"schema":""}46 which represent different assumptions about emission scenarios that might result in a variety of climatic changes over the next 65 years. Here we use RCPs 4.5, 6.0 and 8.5, which assume emission peaks around 2040, 2080 and increases throughout the 21st century respectivelyADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"IPCC","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2013"]]},"publisher":"Cambridge University Press","publisher-place":"New York","title":"Climate Change 2013: the physical science basis: contribution of working group I to the Fifth Assessment Report on The Intergovernmental Panel on Climate Change, IPCC Fifth Assessment Report: Climate Change 2013 (AR5).","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁶","plainTextFormattedCitation":"46","previouslyFormattedCitation":"⁴⁶"},"properties":{"noteIndex":0},"schema":""}46. These time points were chosen because (i) 2020 represents the date when the climate mitigating policies of the Paris Agreement within the United Nations Framework Convention on Climate Change (UNFCCC) will come into actionADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"UNFCCC (United Nations Framework Convention on Climate Change)","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"21st Conference of the Parties","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"publisher-place":"Paris, France","title":"Adoption of the Paris Agreement","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁷","plainTextFormattedCitation":"47","previouslyFormattedCitation":"⁴⁷"},"properties":{"noteIndex":0},"schema":""}47, (ii) 2080 corresponds to the date of the emission peaks modelled according to the RCP 6.0 scenario and (iii) 2050 represents the midpoint between these dates. We use an ensemble of 17 GCMs and pattern scaling to produce monthly mean values of maximum and minimum temperature and monthly totals of rainfall as used in MarkSim. Humidity data were calculated from temperature estimates (see details in section 3). To complement the changes in temperature, relative humidity, and precipitation, we modelled a continued process of global urbanisation until 2080 using a probabilistic machine learning algorithm based on Linard et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.apgeog.2013.07.009","ISSN":"01436228","author":[{"dropping-particle":"","family":"Linard","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"Andrew J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gilbert","given":"Marius","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Geography","id":"ITEM-1","issued":{"date-parts":[["2013","10"]]},"page":"23-32","publisher":"Elsevier Ltd","title":"Modelling spatial patterns of urban growth in Africa","type":"article-journal","volume":"44"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁸","plainTextFormattedCitation":"48","previouslyFormattedCitation":"⁴⁸"},"properties":{"noteIndex":0},"schema":""}48. Here we use urban growth rates projected by the United Nations as a predictor variableADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"United Nations Population Division","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2014"]]},"publisher":"United Nations","publisher-place":"New York","title":"World Urbanization Prospects: The 2014 Revision","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁹","plainTextFormattedCitation":"49","previouslyFormattedCitation":"⁴⁹"},"properties":{"noteIndex":0},"schema":""}49 as well as a range of other critical covariates, as described in van Vuuren et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.apgeog.2013.07.009","ISSN":"01436228","author":[{"dropping-particle":"","family":"Linard","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"Andrew J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gilbert","given":"Marius","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Geography","id":"ITEM-1","issued":{"date-parts":[["2013","10"]]},"page":"23-32","publisher":"Elsevier Ltd","title":"Modelling spatial patterns of urban growth in Africa","type":"article-journal","volume":"44"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁸","plainTextFormattedCitation":"48","previouslyFormattedCitation":"⁴⁸"},"properties":{"noteIndex":0},"schema":""}48.1.3. Mosquito spatial spread dataA unique set of time-series occurrence records for both species were abstracted from KraemerMonath et al.4 and from WilliamsonADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Kraemer","given":"M. U. 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global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus","type":"article-journal","volume":"4"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/sdata.2015.35","author":[{"dropping-particle":"","family":"Kraemer","given":"Moritz U G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sinka","given":"Marianne E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duda","given":"Kirsten A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mylne","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shearer","given":"Freya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Messina","given":"Jane 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Data","id":"ITEM-2","issued":{"date-parts":[["2015"]]},"page":"150035","title":"The global compendium of Aedes aegypti and Ae . albopictus occurrence","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"^4,43","plainTextFormattedCitation":"4,43","previouslyFormattedCitation":"^4,43"},"properties":{"noteIndex":0},"schema":""}4,43, and updated with records obtained from Gasser Hahn et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/jme/tjw072","author":[{"dropping-particle":"","family":"Hahn","given":"Micah B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eisen","given":"Rebecca J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eisen","given":"Lars","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boegler","given":"Karen 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Records were available for Ae. aegypti in the United States from 1995 – 2016 with United States county-specific information regarding whether the species was present or absent; for Ae. albopictus information was available from the United States (1987 – 2013) and from Europe (1979 -2017) (Fig. 1, Extended Data Fig. 2). We considered these time periods because they show consistent expansion of the species distribution as described in Hahn et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/jme/tjw072","author":[{"dropping-particle":"","family":"Hahn","given":"Micah B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eisen","given":"Rebecca J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eisen","given":"Lars","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boegler","given":"Karen A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moore","given":"Chester G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mcallister","given":"Janet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Savage","given":"Harry M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mutebi","given":"John-paul","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Medical Entomology","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"page":"1-7","title":"Reported Distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus in the United States, 1995-2016 (Diptera: Culicidae)","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵⁰","plainTextFormattedCitation":"50","previouslyFormattedCitation":"⁵⁰"},"properties":{"noteIndex":0},"schema":""}50.For the United States, counties were identified as reporting presence of either species in a given year if at least one specimen of any life stage of the mosquito was collected, using any collection methodADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/jme/tjw072","author":[{"dropping-particle":"","family":"Hahn","given":"Micah B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eisen","given":"Rebecca J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eisen","given":"Lars","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boegler","given":"Karen A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moore","given":"Chester G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mcallister","given":"Janet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Savage","given":"Harry M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mutebi","given":"John-paul","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Medical Entomology","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"page":"1-7","title":"Reported Distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus in the United States, 1995-2016 (Diptera: Culicidae)","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵⁰","plainTextFormattedCitation":"50","previouslyFormattedCitation":"⁵⁰"},"properties":{"noteIndex":0},"schema":""}50. Sampling efforts, techniques and temporal resolution were heterogeneous across counties and states in the United States. Therefore, the baseline presence datasets may classify some areas as absent where either of the two Aedes species considered may be present.For Europe, Administrative/Statistical units (NUTS3) were identified as reporting establishment of either species in a given year if immature stages and overwintering were observed, using any collection method. Sampling efforts, techniques, and temporal resolution were heterogeneous across countries and either species may have been absent before investigations were triggered by citizen complaint. Therefore, dates correspond to published reports or expert-shared data (VBORNET, VectorNet), and a species could have established earlier in some locations where regular surveillance had not been implemented. Because we were not able to quantify the sampling biases, we instead employed a sensitivity analysis to account for potential under- or over-reporting (see section 2.4).1.4. Human mobility datasetsOverland human movements are known to drive the importation of both speciesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/s41598-017-12652-5","author":[{"dropping-particle":"","family":"Eritja","given":"Roger","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Palmer","given":"John R B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Roiz","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sanpera-calbet","given":"Isis","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bartumeus","given":"F.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Reports","id":"ITEM-1","issued":{"date-parts":[["2017"]]},"page":"14399","title":"Direct Evidence of Adult Aedes albopictus Dispersal by Car","type":"article-journal","volume":"7"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.3201/eid0303.970309","ISSN":"1080-6040","PMID":"9284377","abstract":"Since its discovery in Houston, Texas, in 1987, the Asian \"tiger mosquito\" Aedes albopictus has spread to 678 counties in 25 states. This species, which readily colonizes container habitats in the peridomestic environment, was probably introduced into the continental United States in shipments of scrap tires from northern Asia. The early pattern of dispersal followed the interstate highway system, which suggests further dispersal by human activities. The Public Health Service Act of 1988 requires shipments of used tires from countries with Ae. albopictus to be treated to prevent further importations. Given the extensive spread of the mosquito in the United States, it is questionable whether such a requirement is still justified. Ae. albopictus, a major biting pest throughout much of its range, is a competent laboratory vector of at least 22 arboviruses, including many viruses of public health importance. Cache Valley and eastern equine encephalomyelitis viruses are the only human pathogens isolated from U.S. populations of Ae. albopictus. There is no evidence that this mosquito is the vector of human disease in the United States.","author":[{"dropping-particle":"","family":"Moore","given":"C G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mitchell","given":"C J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Emerging infectious diseases","id":"ITEM-2","issue":"3","issued":{"date-parts":[["1997"]]},"page":"329-34","title":"Aedes albopictus in the United States: ten-year presence and public health implications.","type":"article-journal","volume":"3"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1186/s13071-015-0793-6","author":[{"dropping-particle":"","family":"Flacio","given":"Eleonora","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Engeler","given":"Lukas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tonolla","given":"Mauro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lüthy","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Patocchi","given":"Nicola","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites and Vectors","id":"ITEM-3","issue":"208","issued":{"date-parts":[["2015"]]},"title":"Strategies of a thirteen year surveillance programme on Aedes albopictus (Stegomyia albopicta) in southern Switzerland","type":"article-journal","volume":"8"},"uris":[""]}],"mendeley":{"formattedCitation":"^38,39,41","plainTextFormattedCitation":"38,39,41","previouslyFormattedCitation":"^38,39,41"},"properties":{"noteIndex":0},"schema":""}38,39,41. Therefore we used human movement data to infer the connectivity between regions as a proxy for importation risk of Ae. aegypti and Ae. albopictus.US commuting data: For the United States, where both species have been spreading successfully, we obtained data on workforce commuting flows from county to county between 2009 – 2013, conducted by the American Community Survey (ACS). Data are freely available at . Here, commuting was defined as a worker’s travel between home and workplace, where the latter refers to the geographical location of the worker’s job. Daytime population refers to the estimated number of people who are residing and working in an area during “daytime working hours”. The data represent 3,134 counties including 50 states and the District of Columbia (DC) but excluding Puerto Rico. The generalisability of this data has been demonstrated in studies that have successfully approximated human movements derived from mobile phone data and predicted the spread of infectious diseasesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nature10856","ISSN":"1476-4687","PMID":"22367540","abstract":"Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.","author":[{"dropping-particle":"","family":"Simini","given":"Filippo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maritan","given":"Amos","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barabási","given":"Albert-László","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-1","issue":"7392","issued":{"date-parts":[["2012","4","5"]]},"page":"96-100","title":"A universal model for mobility and migration patterns.","type":"article-journal","volume":"484"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁴","plainTextFormattedCitation":"24","previouslyFormattedCitation":"²⁴"},"properties":{"noteIndex":0},"schema":""}24. As described below in section 2.3 in detail, we considered gravity and radiation movement models as well as nearest neighbour-type movements for human movement. We used the fitted models from the USA to extrapolate to all other regions in the Americas using the movement package in RADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"M.U.G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"R package version 0.2","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"title":"Movement: Functions for the analysis of movement data in disease modelling and mapping.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵¹","plainTextFormattedCitation":"51","previouslyFormattedCitation":"⁵¹"},"properties":{"noteIndex":0},"schema":""}51.European mobile phone data: For Europe, we obtained mobile phone data (or call detail records, or CDRs) from three different countries where Ae. albopictus is present or has recently been detected: FranceADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/sdata.2015.35","author":[{"dropping-particle":"","family":"Kraemer","given":"Moritz U G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sinka","given":"Marianne E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duda","given":"Kirsten A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mylne","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shearer","given":"Freya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Messina","given":"Jane P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barker","given":"Christopher M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moore","given":"Chester G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carvalho","given":"Roberta G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Coelho","given":"Giovanini E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"Van","family":"Bortel","given":"Wim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hendrickx","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schaffner","given":"F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wint","given":"G.R.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elyazar","given":"Iqbal R F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Teng","given":"Hwa-Jen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Data","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"page":"150035","title":"The global compendium of Aedes aegypti and Ae . albopictus occurrence","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴³","plainTextFormattedCitation":"43","previouslyFormattedCitation":"⁴³"},"properties":{"noteIndex":0},"schema":""}43, PortugalADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/IJERPH15040820","ISSN":"1660-4601","PMID":"29690531","abstract":"The Asian tiger mosquito Aedes albopictus is an invasive mosquito originating from the Asia-Pacific region. This species is of major concern to public and veterinary health because of its vector role in the transmission of several pathogens, such as chikungunya, dengue, and Zika viruses. In Portugal, a National Vector Surveillance Network (REde de VIgilância de VEctores—REVIVE) is responsible for the surveillance of autochthonous, but also invasive, mosquito species at points of entry, such as airports, ports, storage areas, and specific border regions with Spain. At these locations, networks of mosquito traps are set and maintained under surveillance throughout the year. In September 2017, Ae. albopictus was detected for the first time in a tyre company located in the North of Portugal. Molecular typing was performed, and a preliminary phylogenetic analysis indicated a high similarity with sequences of Ae. albopictus collected in Europe. A prompt surveillance response was locally implemented to determine its dispersal and abundance, and adult mosquitoes were screened for the presence of arboviral RNA. A total of 103 specimens, 52 immatures and 51 adults, were collected. No pathogenic viruses were detected. Despite the obtained results suggest low abundance of the population locally introduced, the risk of dispersal and potential establishment of Ae. albopictus in Portugal has raised concern for autochthonous mosquito-borne disease outbreaks.","author":[{"dropping-particle":"","family":"Osório","given":"Hugo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zé-Zé","given":"Líbia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neto","given":"Maria","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silva","given":"Sílvia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marques","given":"Fátima","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silva","given":"Ana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alves","given":"Maria","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Environmental Research and Public Health 2018, Vol. 15, Page 820","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2018"]]},"page":"820","title":"Detection of the Invasive Mosquito Species Aedes (Stegomyia) albopictus (Diptera: Culicidae) in Portugal","type":"article-journal","volume":"15"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵²","plainTextFormattedCitation":"52","previouslyFormattedCitation":"⁵²"},"properties":{"noteIndex":0},"schema":""}52, and SpainADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/sdata.2015.35","author":[{"dropping-particle":"","family":"Kraemer","given":"Moritz U G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sinka","given":"Marianne E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duda","given":"Kirsten A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mylne","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shearer","given":"Freya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Messina","given":"Jane P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barker","given":"Christopher M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moore","given":"Chester G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carvalho","given":"Roberta G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Coelho","given":"Giovanini E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"Van","family":"Bortel","given":"Wim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hendrickx","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schaffner","given":"F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wint","given":"G.R.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elyazar","given":"Iqbal R F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Teng","given":"Hwa-Jen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Data","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"page":"150035","title":"The global compendium of Aedes aegypti and Ae . albopictus occurrence","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴³","plainTextFormattedCitation":"43","previouslyFormattedCitation":"⁴³"},"properties":{"noteIndex":0},"schema":""}43. CDR data contain the time at which a call was made or a text message was sent, the duration of the call, and the code of the cell in which communication started. The cell corresponds to an area covered by a specific mobile phone tower that serves a particular area. This means that the spatial resolution is restricted to the tower area, the specific location of each individual in the dataset cannot be ascertained. As our analysis was performed at the district level, all users’ activity profiles were aggregated up to the district level, which is generally larger than cell tower areas. We thereby obtained a connectivity matrix that shows the connections made between each district i to each district j within each respective country. For Portugal, data were available from over one million mobile phone users between April 2006 and March 2007 (12 months). In Spain, CDRs were extracted from 1,034,430 users over three months between November 2007 and January 2008. In France we had the largest sample of 5,695,974 users, collected between September 2007 and mid-October 2007 covering the entire country. Other aspects of the collection and processing methods have been described in detail elsewhereADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1003716","ISSN":"1553-7358","author":[{"dropping-particle":"","family":"Tizzoni","given":"Michele","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bajardi","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Decuyper","given":"Adeline","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kon Kam King","given":"Guillaume","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schneider","given":"Christian M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blondel","given":"Vincent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smoreda","given":"Zbigniew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Colizza","given":"Vittoria","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS Computational Biology","editor":[{"dropping-particle":"","family":"Salathé","given":"Marcel","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issue":"7","issued":{"date-parts":[["2014","7","10"]]},"page":"e1003716","title":"On the use of human mobility proxies for modeling epidemics","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"²³","plainTextFormattedCitation":"23","previouslyFormattedCitation":"²³"},"properties":{"noteIndex":0},"schema":""}23. We used the fitted models from Europe to extrapolate to all other regions in Europe, using the movement package in RADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"M.U.G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"R package version 0.2","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"title":"Movement: Functions for the analysis of movement data in disease modelling and mapping.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵¹","plainTextFormattedCitation":"51","previouslyFormattedCitation":"⁵¹"},"properties":{"noteIndex":0},"schema":""}51.Human movement data for Asia: Mobility matrices for Asia are inferred from data from Chinese users of Baidu, the largest location-based service (LBS) in China. Baidu offers a large variety of apps and software for mobile devices and personal computers, mostly for online searching. We extracted GPS data from 23 April 2013 to 30 April 2014 (about 400 million users in China). The raw data was collected at the county level (n = 2,959) and aggregated to the prefecture level (345 prefectures). We then estimated daily flows of people between each pair of counties and aggregated this information per year. Movement is recorded in the Baidu data such that on each day if a user was observed at locations A->B->C, then A->B and A->C are counted which may produce biased population flow estimates. To explore potential bias in the data we compared the data derived from Baidu to a complete dataset of taxi-based GPS locations in the capital city of Hunan province, covering a one week period (full details below). The correlation of origin-to-destination flows in the city between the Baidu data and the complete taxi GPS data was very high (R2 = 0.99).Baidu data validation: To verify the validity of the Baidu LBS data, we obtained a complete dataset of GPS locations for all taxis in Changsha city (capital of Hunan Province, population: 7 million) in 2014. The location of each taxi is recorded for regulatory reasons using a GPS device in each taxi. The location is updated every 30 seconds. There were approximately 7,000 taxis in Changsha resulting in 20.16 million records (7000*24*60*2) on a daily basis. The status of the cab was also recorded, such as the locations where passengers get on and off. These data are then used to extract the movements between the five districts in the main area of Changsha: Kaifu district, Furong district, Yuhua district, Tianxin district, and Yuelu district. For the purpose of comparison, one week’s data (April 4 to April 17, 2016) were extracted and analysed. The movements were normalized and then compared with the same week in 2014 from the Baidu LBS data. The correlation between the mobility estimates extracted from the Baidu LBS data and from the taxi’s GPS data for Changsha city is presented in Extended Data Fig. 9. There is a high level of similarity between the two datasets, with a correlation coefficient of 0.99 (p=0.001). We subsequently used the fitted models from China to extrapolate to other regions in Asia and Oceania again using the movement package in RADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"M.U.G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"R package version 0.2","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"title":"Movement: Functions for the analysis of movement data in disease modelling and mapping.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵¹","plainTextFormattedCitation":"51","previouslyFormattedCitation":"⁵¹"},"properties":{"noteIndex":0},"schema":""}51.Human movement data for Africa: To calibrate the gravity and radiation models for Africa, we used aggregated and de-identified mobile phone-derived mobility estimates at the constituency level from Namibia between 1 October 2010 and 30 September 2011. These data represent the proportion of time that unique subscriber identity module (SIM) cards in each constituency spend in all other constituencies, as described in detail in Jones & Thornton (2000)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1004846","author":[{"dropping-particle":"","family":"Ruktanonchai","given":"Nick W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Deleenheer","given":"Patrick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"Andrew J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alegana","given":"Victor A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Caughlin","given":"T Trevor","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Erbach-schoenberg","given":"Elisabeth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Louren?o","given":"Christopher","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ruktanonchai","given":"Corrine W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLOS Computational Biology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2016"]]},"page":"e1004846","title":"Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data","type":"article-journal","volume":"12"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵³","plainTextFormattedCitation":"53","previouslyFormattedCitation":"⁵³"},"properties":{"noteIndex":0},"schema":""}53. We used this data set from Namibia because it was openly available and because it offered the best spatial and temporal resolution compared to census-derived data. We then used the fitted models to extrapolate to all other regions in Africa using the movement package in RADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"M.U.G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"R package version 0.2","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"title":"Movement: Functions for the analysis of movement data in disease modelling and mapping.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵¹","plainTextFormattedCitation":"51","previouslyFormattedCitation":"⁵¹"},"properties":{"noteIndex":0},"schema":""}51. Systematic surveys of cross-border human movements were not available at the time of the study and for the study regions.It is possible that there are significant differences between regions in terms of mobility, but unfortunately no sufficiently widespread and well-resolved data source was available to test this. Our model captured the spread process of Aedes mosquitoes using a variety of human movement data, including both CDR data and commuting data. To assess the generalizability of our results we applied the model fitted to commuting data in the USA to the range expansion process observed in Europe. The predictive ability of this cross-continental validation indicates that the mobility data used are sufficiently robust in the context of this study (Extended Data Fig. 8). However, we note there may be several limitations to using commuting data to infer vector introductions as they overly emphasize work-related movements. To test whether our model would perform well even in the absence of human movement data, we performed a cross validation that uses only distance and adjacency matrices which only marginally reduces predictability (Extended Data Fig. 12). Despite this, such data has indeed been used in the United States to successfully predict the long distance spread of infectious diseases. We are therefore confident that such data can be applied to predict both short and long distance spread in the USAADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1126/science.1125237","ISSN":"1095-9203","PMID":"16574822","abstract":"Quantifying long-range dissemination of infectious diseases is a key issue in their dynamics and control. Here, we use influenza-related mortality data to analyze the between-state progression of interpandemic influenza in the United States over the past 30 years. Outbreaks show hierarchical spatial spread evidenced by higher pairwise synchrony between more populous states. Seasons with higher influenza mortality are associated with higher disease transmission and more rapid spread than are mild ones. The regional spread of infection correlates more closely with rates of movement of people to and from their workplaces (workflows) than with geographical distance. Workflows are described in turn by a gravity model, with a rapid decay of commuting up to around 100 km and a long tail of rare longer range flow. A simple epidemiological model, based on the gravity formulation, captures the observed increase of influenza spatial synchrony with transmissibility; high transmission allows influenza to spread rapidly beyond local spatial constraints.","author":[{"dropping-particle":"","family":"Viboud","given":"Cécile","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bj?rnstad","given":"Ottar N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Simonsen","given":"Lone","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miller","given":"Mark a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grenfell","given":"Bryan T","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Science","id":"ITEM-1","issue":"5772","issued":{"date-parts":[["2006","4","21"]]},"page":"447-51","title":"Synchrony, waves, and spatial hierarchies in the spread of influenza.","type":"article-journal","volume":"312"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵⁴","plainTextFormattedCitation":"54","previouslyFormattedCitation":"⁵⁴"},"properties":{"noteIndex":0},"schema":""}54. Similarly, CDR data has been used to describe the spread of pathogens such as influenza in EuropeADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1003716","ISSN":"1553-7358","author":[{"dropping-particle":"","family":"Tizzoni","given":"Michele","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bajardi","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Decuyper","given":"Adeline","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kon Kam King","given":"Guillaume","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schneider","given":"Christian M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blondel","given":"Vincent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smoreda","given":"Zbigniew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Colizza","given":"Vittoria","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS Computational Biology","editor":[{"dropping-particle":"","family":"Salathé","given":"Marcel","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issue":"7","issued":{"date-parts":[["2014","7","10"]]},"page":"e1003716","title":"On the use of human mobility proxies for modeling epidemics","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"²³","plainTextFormattedCitation":"23","previouslyFormattedCitation":"²³"},"properties":{"noteIndex":0},"schema":""}23. As new data become available, our model is flexible enough to incorporate them and estimates of the predicted range expansion of Ae. aegypti and Ae. albopictus can be updated. There was also no suitable data available on cross border movements that could improve estimates of between-country spread (see section 2.4. for a sensitivity analysis).2. Model fitting to data2.1 Description of speed of dispersal:To understand the past range expansion of both species and to provide basic summary statistics of the speed of dispersal over time in areas where sufficient observations were available, we use the methods of spread rate measurements employed by Tisseuil et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1111/ecog.01393","ISBN":"1600-0587","ISSN":"16000587","abstract":"Community assembly rules have been extensively studied, but its association with regional environmental variation, while land use history remains largely unexplored. Land use history might be especially important in Mediterranean forests, considering their historical deforestation and recent afforestation. Using forest inventories and historical (1956) and recent (2000) land cover maps, we explored the following hypotheses: 1) woody species assembly is driven by environmental factors, but also by historical landscape attributes; 2) recent forests exhibit lower woody species richness than pre-existing due to the existence of colonization credits; 3) these credits are modulated by species’ life-forms and dispersal mechanisms. We examined the association of forest historical type (pre-existing versus recent) with total species richness and that of diverse life-forms and dispersal groups, also considering the effects of current environment and past landscape factors. When accounting for these effects, no significant differences in woody species richness were found between forest historical types except for vertebrate-dispersed species. Species richness of this group was affected by the interaction of forest historical type with distance to coast and rainfall: vertebrate-dispersed species richness increased with rainfall and distance to the coast in recent forests, while it was higher in dryer sites in pre-existing forests. In addition, forest historical types showed differences in woody species composition associated to diverse environmental and past landscape factors. In view of these results we can conclude that: 1) community assembly in terms of species richness is fast enough to exhaust most colonization credit in recent Mediterranean forests except for vertebrate-dispersed species; 2) for these species, colonization credit is affected by the interplay of forest history and a set of proxies of niche and landscape constraints of species dispersal and establishment; 3) woody species assemblage is mostly shaped by the species’ ecological niches in these forests.","author":[{"dropping-particle":"","family":"Tisseuil","given":"Clément","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gryspeirt","given":"Aiko","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lancelot","given":"Renaud","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pioz","given":"Maryline","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liebhold","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gilbert","given":"Marius","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Ecography","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2015"]]},"page":"409-418","title":"Evaluating methods to quantify spatial variation in the velocity of biological invasions","type":"article-journal","volume":"39"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵⁵","plainTextFormattedCitation":"55","previouslyFormattedCitation":"⁵⁵"},"properties":{"noteIndex":0},"schema":""}55. For each species and study area, the centroids of the spatial units where the species were observed were re-projected in a metric system (epsg 102003 in the US, and epsg 3035 in Europe) and the first date of detection in each centroid was interpolated on a 10 km resolution grid using thin plate spline regression (TPSR). The local slope of the surface was measured by a 3 x 3 moving windows filter, and the resulting friction surface (time / distance) was smoothed by an average 11 x 11 cell filter to prevent local null frictions values. The local spread rate was then obtained by taking the inverse of the friction. This measure was computed within a mask, which was obtained by kernel density smoothing of the centroids of spatial units where the species were observed. 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A density threshold of 2.9 points per 10,000 km2 was chosen to delineate the mask, which was the maximum threshold value allowing the inclusions of all observation points in the mask in both the US and EU.2.2. Mosquito environmental niche modellingTo predict the likely future distributions of both species independently (in years 2020, 2050 and 2080), we first fitted species distribution models to data from the present day. This approach built on previous workADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Kraemer","given":"M. U. 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BRTs combine strengths from regression trees and machine learning (gradient boosting) and are able to accommodate non-linear relationships to identify the environmental niche in which the environment is suitable for the species in question. After an initial regression tree is fitted and iteratively improved upon in a forward stepwise manner (boosting) by minimising the variation in the response variable not explained by the model at each iteration. This approach has been shown to simultaneously fit complex non-linear response functions efficiently while guarding against over-fitting.We first developed a baseline scenario for the year 2015, using the global dataset of Ae. aegypti and Ae. albopictus occurrence (section 1.1)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/sdata.2015.35","author":[{"dropping-particle":"","family":"Kraemer","given":"Moritz U G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sinka","given":"Marianne E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duda","given":"Kirsten A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mylne","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shearer","given":"Freya 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In a BRT modelling framework pseudo-absences need to be generated to allow for discrimination between areas where the mosquitoes can persist, and to identify biases in reportingADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"1051-0761","PMID":"19323182","abstract":"Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.","author":[{"dropping-particle":"","family":"Phillips","given":"Steven J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dudík","given":"Miroslav","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elith","given":"Jane","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Graham","given":"Catherine H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lehmann","given":"Anthony","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leathwick","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ferrier","given":"Simon","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Ecological applications","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2009","1"]]},"page":"181-97","title":"Sample selection bias and presence-only distribution model: implications for background and pseudo-absence data.","type":"article-journal","volume":"19"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵⁸","plainTextFormattedCitation":"58","previouslyFormattedCitation":"⁵⁸"},"properties":{"noteIndex":0},"schema":""}58. We used the approach previously described in and applied by Kraemer et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Kraemer","given":"M. U. 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One of the principal determinants of Aedes survival is temperature, which has been associated with seasonal changes in Aedes populations and limits their geographical distribution. The effects of temperature and other sources of mortality have been studied in the field, often via mark-release-recapture experiments, and under controlled conditions in the laboratory. Survival results differ and reconciling predictions between the two settings has been hindered by variable measurements from different experimental protocols, lack of precision in measuring survival of free-ranging mosquitoes, and uncertainty about the role of age-dependent mortality in the field.\n\nMETHODS: Here we apply generalised additive models to data from 351 published adult Ae. aegypti and Ae. albopictus survival experiments in the laboratory to create survival models for each species across their range of viable temperatures. These models are then adjusted to estimate survival at different temperatures in the field using data from 59 Ae. aegypti and Ae. albopictus field survivorship experiments. The uncertainty at each stage of the modelling process is propagated through to provide confidence intervals around our predictions.\n\nRESULTS: Our results indicate that adult Ae. albopictus has higher survival than Ae. aegypti in the laboratory and field, however, Ae. aegypti can tolerate a wider range of temperatures. A full breakdown of survival by age and temperature is given for both species. The differences between laboratory and field models also give insight into the relative contributions to mortality from temperature, other environmental factors, and senescence and over what ranges these factors can be important.\n\nCONCLUSIONS: Our results support the importance of producing site-specific mosquito survival estimates. By including fluctuating temperature regimes, our models provide insight into seasonal patterns of Ae. aegypti and Ae. albopictus population dynamics that may be relevant to seasonal changes in dengue virus transmission. Our models can be integrated with Aedes and dengue modelling efforts to guide and evaluate vector control, better map the distribution of disease and produce early warning systems for dengue epidemics.","author":[{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johansson","given":"Michael a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guerra","given":"Carlos a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhatt","given":"Samir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Delatte","given":"Hélène","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grech","given":"Marta G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leisnham","given":"Paul T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maciel-de-Freitas","given":"Rafael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Styer","given":"Linda M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"Thomas W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"Simon I","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & vectors","id":"ITEM-1","issued":{"date-parts":[["2013","1"]]},"page":"351","title":"Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings.","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁵","plainTextFormattedCitation":"45","previouslyFormattedCitation":"⁴⁵"},"properties":{"noteIndex":0},"schema":""}45 with equal ratio between presence and absence points and no threshold being applied. From that we constructed 100 sub-models to derive the mean prediction map and model-fitting uncertainty using the SEEG-SDM package in RADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"R Core Team","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"R foundation for statistical computing","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"title":"R: A language and environment for computing. Vienna, Austria.","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"author":[{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"R package version 0.1-3","id":"ITEM-2","issued":{"date-parts":[["2014"]]},"title":"Streamlined functions for species distribution modelling in the seeg research group","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"^59,60","plainTextFormattedCitation":"59,60","previouslyFormattedCitation":"^59,60"},"properties":{"noteIndex":0},"schema":""}59,60.2.3. Human mobility modellingGiven the heterogeneous abundance of both speciesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1003327","ISBN":"1553-7358 (Electronic)\\n1553-734X (Linking)","ISSN":"1553734X","PMID":"24348223","abstract":"The Ross-Macdonald model has dominated theory for mosquito-borne pathogen transmission dynamics and control for over a century. The model, like many other basic population models, makes the mathematically convenient assumption that populations are well mixed; i.e., that each mosquito is equally likely to bite any vertebrate host. This assumption raises questions about the validity and utility of current theory because it is in conflict with preponderant empirical evidence that transmission is heterogeneous. Here, we propose a new dynamic framework that is realistic enough to describe biological causes of heterogeneous transmission of mosquito-borne pathogens of humans, yet tractable enough to provide a basis for developing and improving general theory. The framework is based on the ecological context of mosquito blood meals and the fine-scale movements of individual mosquitoes and human hosts that give rise to heterogeneous transmission. Using this framework, we describe pathogen dispersion in terms of individual-level analogues of two classical quantities: vectorial capacity and the basic reproductive number, R0. Importantly, this framework explicitly accounts for three key components of overall heterogeneity in transmission: heterogeneous exposure, poor mixing, and finite host numbers. Using these tools, we propose two ways of characterizing the spatial scales of transmission--pathogen dispersion kernels and the evenness of mixing across scales of aggregation--and demonstrate the consequences of a model's choice of spatial scale for epidemic dynamics and for estimation of R0, both by a priori model formulas and by inference of the force of infection from time-series data.","author":[{"dropping-particle":"","family":"Perkins","given":"T. Alex","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"Thomas W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Menach","given":"Arnaud","non-dropping-particle":"Le","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS Computational Biology","id":"ITEM-1","issue":"12","issued":{"date-parts":[["2013"]]},"page":"e1003327","title":"Heterogeneity, mixing, and the spatial scales of mosquito-borne Ppathogen transmission","type":"article-journal","volume":"9"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶¹","plainTextFormattedCitation":"61","previouslyFormattedCitation":"⁶¹"},"properties":{"noteIndex":0},"schema":""}61 as well as the low probability of their surviving slower and longer transits, the chance of a species being introduced following any single translocation event is low. Hence we used relatively long time steps (yearly) and generalized human movement models fitted to a variety of data sources to understand the spatial spread patterns of Ae. aegypti and Ae. albopictus.We incorporated three distinct human movement models that act at different scales, since we are uncertain a priori which type of human movement will be most associated with mosquito spread. We considered (i) a gravity model, (ii) a radiation model, (iii) an adjacency network model and (iv) un-transformed great-circle distance. Each of these models have been shown to be useful depending on the local context to infer regular daily commuting patterns, longer-term movements, and as general descriptions of human mobilityADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1126/science.1245200","ISSN":"1095-9203","PMID":"24337289","abstract":"The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic-mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.","author":[{"dropping-particle":"","family":"Brockmann","given":"Dirk","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Helbing","given":"Dirk","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Science","id":"ITEM-1","issue":"6164","issued":{"date-parts":[["2013","12","13"]]},"page":"1337-42","title":"The hidden geometry of complex, network-driven contagion phenomena.","type":"article-journal","volume":"342"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/nature10856","ISSN":"1476-4687","PMID":"22367540","abstract":"Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.","author":[{"dropping-particle":"","family":"Simini","given":"Filippo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maritan","given":"Amos","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barabási","given":"Albert-László","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-2","issue":"7392","issued":{"date-parts":[["2012","4","5"]]},"page":"96-100","title":"A universal model for mobility and migration patterns.","type":"article-journal","volume":"484"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1007/s12080-014-0245-5","ISSN":"1874-1738","author":[{"dropping-particle":"","family":"Jongejans","given":"Eelke","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Skarpaas","given":"Olav","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ferrari","given":"Matthew J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Long","given":"Eric S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dauer","given":"Joseph T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schwarz","given":"Carrie M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rauschert","given":"Emily S. 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First, the gravity model, assumes that fluxes between two areas i and j are Ti,j =kNiαNjβdi,jγ, where N represents human population size and d is great circle distance between two locations, and k, α, β, and γ are parameters to be fitADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1004267","ISSN":"1553-7358","author":[{"dropping-particle":"","family":"Wesolowski","given":"Amy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"O’Meara","given":"Wendy Prudhomme","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eagle","given":"Nathan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"Andrew J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckee","given":"Caroline O.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLOS Computational Biology","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2015"]]},"page":"e1004267","title":"Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa","type":"article-journal","volume":"11"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e.","author":[{"dropping-particle":"","family":"Wesolowski","given":"Amy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckee","given":"Caroline O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bengtsson","given":"Linus","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wetter","given":"Erik","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Xin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"Andrew J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLOS Current Outbreaks","id":"ITEM-2","issue":"Edition 1","issued":{"date-parts":[["2014"]]},"title":"Commentary: Containing the Ebola outbreak – the potential and challenge of mobile network data","type":"article-journal","volume":"Sep 29"},"uris":[""]}],"mendeley":{"formattedCitation":"^64,65","plainTextFormattedCitation":"64,65","previouslyFormattedCitation":"^64,65"},"properties":{"noteIndex":0},"schema":""}64,65. The gravity model emphasises the attractive power of large population centres. Second, the radiation model assumes fluxes to be Ti,j = TiNiNjNi+si,jNi+Nj+si,j, where Ti is the number of individuals leaving area i and sij is the total population in the circle centered at i with radius di,j excluding the population of the two areas i and j. The radiation model considers not only distance and population sizes at origin and destination but also the cumulative population at a lesser distance from the origin than the destinationADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nature10856","ISSN":"1476-4687","PMID":"22367540","abstract":"Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.","author":[{"dropping-particle":"","family":"Simini","given":"Filippo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"González","given":"Marta C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maritan","given":"Amos","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barabási","given":"Albert-László","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-1","issue":"7392","issued":{"date-parts":[["2012","4","5"]]},"page":"96-100","title":"A universal model for mobility and migration patterns.","type":"article-journal","volume":"484"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁴","plainTextFormattedCitation":"24","previouslyFormattedCitation":"²⁴"},"properties":{"noteIndex":0},"schema":""}24. Consequently, this model considers not only the origin and destination but also the landscape of ‘intervening opportunities’ between them. Third, adjacency networks encode the number of district borders an individual would need to cross to move from one district to another. Thus, this metric reflects the neighbourhood effect. Finally, we computed the great-circle distance between each pair of locations and used that as a metric of mobility in and of itself ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1097/QAD.0b013e328359a904","ISSN":"1473-5571","PMID":"22951637","abstract":"OBJECTIVE/DESIGN: The global spread of HIV-1 main group (group M) has resulted in differential distributions of subtypes and recombinants, with the greatest diversity being found in sub-Saharan Africa. The explanations for the current subtype distribution patterns are likely multifactorial, but the promotion of human migrations and movements through transportation link availability and quality, summarized through 'accessibility', have been consistently cited as strong drivers. We sought to address the question of whether accessibility has been a significant factor in HIV-1 spread across mainland Africa through spatial analyses of molecular epidemiology, transport network and land cover data.\n\nMETHODS: The distribution of HIV-1 subtypes and recombinants in sub-Saharan Africa for the period 1998-2008 was mapped using molecular epidemiology data at a finer level of detail than ever before. Moreover, hypotheses on the role of distance, road network structure and accessibility in explaining the patterns seen were tested using spatial datasets representing African transport infrastructure, land cover and an accessibility model of landscape travel speed.\n\nRESULTS: Coherent spatial patterns in HIV-1 subtype distributions across the continent exist, and a substantial proportion of the variance in the distribution and diversity pattern seen can be explained by variations in regional spatial accessibility.\n\nCONCLUSION: The study confirms quantitatively the influence of transport infrastructure on HIV-1 spread within Africa, presents an approach for examining potential future impacts of road development projects and, more generally, highlights the importance of accessibility in the spread of communicable diseases.","author":[{"dropping-particle":"","family":"Tatem","given":"Andrew J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hemelaar","given":"Joris","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gray","given":"Rebecca R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salemi","given":"Marco","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIDS (London, England)","id":"ITEM-1","issue":"18","issued":{"date-parts":[["2012","11","28"]]},"page":"2351-60","title":"Spatial accessibility and the spread of HIV-1 subtypes and recombinants.","type":"article-journal","volume":"26"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1098/rsif.2015.0468","author":[{"dropping-particle":"","family":"Kraemer","given":"M.U.G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perkins","given":"T.A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cummings","given":"D.A.T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zakar","given":"R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"D.L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reiner","given":"R.C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of the Royal Society Interface","id":"ITEM-2","issued":{"date-parts":[["2015"]]},"page":"20150468","title":"Big city, small world: density, contact rates, and transmission of dengue across Pakistan","type":"article-journal","volume":"12"},"uris":[""]}],"mendeley":{"formattedCitation":"^32,66","plainTextFormattedCitation":"32,66","previouslyFormattedCitation":"^32,66"},"properties":{"noteIndex":0},"schema":""}32,66.For each second Administrative unit (county/municipality) in the world, we determined the total human population size using gridded population estimates and calculated the great-circle distance between the centroids of each pair of districts within each continentADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","author":[{"dropping-particle":"","family":"WorldPop project","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["0"]]},"title":"WorldPop","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶⁷","plainTextFormattedCitation":"67","previouslyFormattedCitation":"⁶⁷"},"properties":{"noteIndex":0},"schema":""}67. Gravity and radiation model parameters were fitted by maximum likelihood methods to the empirical data described above using the movement R packageADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"M.U.G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"R package version 0.2","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"title":"Movement: Functions for the analysis of movement data in disease modelling and mapping.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵¹","plainTextFormattedCitation":"51","previouslyFormattedCitation":"⁵¹"},"properties":{"noteIndex":0},"schema":""}51. National adjacency networks were computed using administrative boundary data from the GADM dataset (). To account for neighbourhood effects of spread and for the potential importance of within-country and between-country movements, we constructed adjacency matrices that were disaggregated into three binary connectivity matrices with connectivity degrees of one (i.e., districts share a border), two (i.e., districts share a common neighbour), and three (i.e., more than two degrees away).2.4. Mosquito spread modellingLet xi(t) be the Aedes population status of district i at time t (i.e., a binary variable takes the value 1 if there were Aedes mosquitoes that time, and 0 otherwise). Given the nature of the dataset collected, we assumed that all data points represented detection of established populations and thus assumed continuous presence of the species for the first and last reported occurrences. We used a standard logistic model to characterize the probability that some district j will become occupied at time t:logitPxjt= 1xjt-1= 0=β0+k=1nβkYj,t(k)where Yj,t(k) corresponds to the value of explanatory variable k in district j at time t. Explanatory variables included in this analysis were the predicted vector habitat suitability (i.e. suitability for establishment of an introduced vector, 2.1.) and connectivity between infested and non-infested districts (i.e. probability of introduction of a vector). Separate metrics of connectivity were defined for each human movement model (2.2.). From each human movement model, a connectivity matrix Aij(k) was calculated for each location i and j. A corresponding covariate for the occupation model was then computed to represent the global force of importation, exerted from all other infested districts to j: Yj,t(k) =iAijkxit-1.These models were re-fit in each successive year separately for the North American and European datasets, and for each vector species, using all available data up to that year. Model selection was done through backward selection using Akaike Information Criterion (AIC).ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Hastie","given":"T.J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tibshirani","given":"R.J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"edition":"Vol. 43","id":"ITEM-1","issued":{"date-parts":[["1990"]]},"publisher":"CRC Press","title":"Generalized additive models","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶⁸","plainTextFormattedCitation":"68","previouslyFormattedCitation":"⁶⁸"},"properties":{"noteIndex":0},"schema":""}68 The fitted model was then evaluated prospectively over the next year by comparing predicted presence or absence with observations, thereby allowing us to evaluate and validate model performance over time. For model evaluation we considered all locations (i.e. 3,134 counties in the USA, 1,587 NUTS in Europe). This model evaluation was used to identify the best explanatory variables to include in the Aedes spread model. Model evaluation was performed using receiver operating characteristic curves (ROC curves) (Extended Data Fig. 7) and model accuracy was characterized comparing the predicted probabilities of first detection vs the response (Extended Data Fig. 6). We calculated the probability of first detection pw predicted by the model for each district-year that had not yet reported mosquitoes. We then partitioned district-years into eight groups with predicted probability in the range of 0-1%, 1-5%, 5-10%, 10-15%, 15-20%, 20-25%, 25-35%, and 35-100%. For each group, we calculated the mean predicted probability and compared it with the proportion of district-years in the group in which range expansion was observed. Our model assumes that each mosquito species will persist in an area once detected, whilst there are some examples of incursions apparently having been successfully eradicated or died out. It is possible that this assumption could result in inflated predictions of the rate of spread, due to an overestimated number of source populations for each potential invasion event. However, it should be noted that this overestimate of the number of source populations would also be present in the training data, and would be at least partially absorbed into estimates of the probabilities of importation. Insufficient data were available to test or account for this potential bias, but based on additional experiments, we do not anticipate our estimates to greatly overpredict Aedes presence (see section: sensitivity analyses and sampling bias). Cross-validation: To test whether the spread between countries is different to the spread within countries, we used the multi-country dataset from Ae. albopictus in Europe and varied the relative frequency of within- and between-country mobility by decreasing movement between countries by 20%, 50%, and 70%. The results were then compared with a baseline, in which predicted within-country movement is the same as between-country movement (Extended Data Fig. 11). We also performed sensitivity analyses to evaluate how a model including human movements compares to single variable models that have objective measurements such as great circle distance and adjacency. A model that includes human movements only slightly increased predictive performance (Extended Data Fig. 12).Sensitivity analyses and sampling bias: Surveillance efforts to detect Ae. aegypti and Ae. albopictus may vary in time and space due to gradual progressive improvements as a result of technology trapping technology, general expertise, or in response to specific events. Three types of possible changes in surveillance could bias the estimates of our spread model: (1) spatial expansion of surveillance system coverage to new areas; (2) intensification of sampling effort within areas where the surveillance system already operates; and (3) changes in sampling methods within areas where the surveillance system already operates that make it more or less likely to detect either Ae. aegypti or Ae. albopictus. To address each of these, we completed sensitivity analyses to understand how possible changes in surveillance may affect the inference about spread in the future.Expansions of the surveillance system can be definitively distinguished from true known expansions of the vectors by comparing the state transitions of areas in longitudinal datasets, such as our Ae. albopictus dataset in Europe between the years of 2013 and 2017. Areas that first report absence of the species (often for multiple years) and later report presence are as close to a clear example of introduction as possible and give a reasonable estimate of the arrival date. Conversely, if an area’s first report is presence of the species, the species’ arrival date may have been estimated later than it truly occurred.Firstly, the existence of such longitudinal records in the Ae. albopictus database in Europe is strong evidence that the distribution of the species is expanding, however to test if expanding surveillance efforts is a contributing factor to the observed rate of spread we compared our original model fit to the full Ae. albopictus in Europe dataset, as used in our main analysis (model 1), with a model fit only to the data points that have strong evidence for a specific introduction date (i.e., report absence before presence; model 2). We tabulated data from Ae. albopictus in Europe where information was available whether there was ongoing surveillance prior to the reporting of the species (transition from absence to presence). Such data was available for 179 out of 600 observations between 2013 – 2018, a time period where 400 new regions reported the presence of the species making our sub-sample about 50% of all new invasions. This data was available at higher spatial resolution that the full Ae. albopictus dataset for Europe. 75% of these records are from locations of most recent spread in France and Germany. Finally, as model 2 was fit to data from a narrower date range we also consider a third model (model 3) which was fit to both occurrence and longitudinal data but only from the more recent date range (Extended Data Tab. 3). If expansion of surveillance efforts is a contributing factor to the observed rate of spread in the data, then we would expect Model 2 to predict a significantly lower rate of spread than Models 1 or 3 (our null hypothesis). Each of these models were fit to the above datasets, then used to simulate Ae. albopictus spread from a common baseline (based on occurrence and longitudinal data at the end of 2012) for five years between 2013 and 2017 as described previously. The predicted total number of new districts infested of this period was calculated and is shown in Extended Data Tab. 4. Note that comparison of goodness of fit metrics for these models was not possible since the models were fit to different datasets.Contrary to the expectation that more precise dates of invasion would lead to conclusions of slower rates of spread, this sensitivity exercise found that restricting the model to just areas where the date of introduction is known significantly increases the predicted rate of spread. Thus, this exercise rejects our above null hypothesis. This effect was also independent of the time period of the fitting data (similar results for Model 1 and Model 3). These results suggest that it is more likely that true spread of Ae. albopictus is outpacing expansion of mosquito surveillance, and if longitudinal surveillance was in place everywhere, the observed rates of spread would be greater. We therefore believe that the currently implemented model is a conservative estimate of spread of these species that is not highly affected by changes in spatial coverage of surveillance systems and provides the most robust estimates of spread over these time periods given the available data. Given the limited number of years of data available to fit Model 2, we believe that Model 1 provides the most reliable estimates of future spread.Intensification in sampling effort and technological advancements in collection methods may affect the probability of detection of a species in earlier in their invasion process vs today. Here we test both hypotheses through inclusion of different terms in our spread model regression and compare such models to the null of no changes in surveillance intensity over time (as currently implemented in our main analysis). To represent increases or decreases in surveillance over time, we include the spline-smoothed year of detection as a variable in the regression analysis. To represent step changes in surveillance efforts in response to specific events we include a factor variable; either before the 2003 peak in West Nile Virus cases in the USA, or after 2003 (only for models in USA). Internal cross validation was then used to compare the predictive performance of these three models with evaluation on three-year-lookahead holdout sets, subject to a minimum of 10 consecutive years of data to fit the models. Model predictive performance was then compared using deviance from observed values in the holdout set.This showed that for all species in all continents, the inclusion of a temporal (Year) term reduced predictive accuracy (increased deviance). This was the case for both gradual change over time (s(Year)) and for breakpoint changes in response to specific events (Year > 2003). As a result, we conclude that there is no evidence for temporal changes in sampling effort in any of the datasets concerned and therefore do not include such terms in our final predictions (Extended Data Tab. 5).Finally, there is a possibility that changes in general vector surveillance strategies could have led to changes that affected the probability of detection of one species more than the other. Such differential biases could undermine our inter-species spread rate comparison. One key period of concern is around the 2003 West Nile Virus (WNV) outbreak in the US where vector surveillance may have prioritized trapping in more rural environments to optimize detection of various Culex species. Such a focus on rural environments may have led to relative increases in sampling intensity of Ae. albopictus and relative reductions in sampling intensities for Ae. aegypti. To test this hypothesis, we follow a similar approach to the above analysis, where covariates for “before” and “after” the 2003 WNV outbreak are included in the USA spread model for each species. If the above hypothesis is true, such terms should i) have larger “after” values than “before” values in the Ae. albopictus model and vice versa in the Ae. aegypti model, and ii) improve model prediction accuracy.The best fits from the Ae. aegypti and Ae. albopictus spread models in the USA show that detection of Ae. aegypti marginally increased relative to Ae. albopictus (positive model coefficients for post-2003 term in Ae. aegypti, negative in Ae. albopictus) (Extended Data Tab. 6). However, as previously stated, inclusion of such changes in surveillance quality over time reduces the model predictive performance (increase in deviance for both species) and it is therefore most likely that any changes that did occur to Aedes surveillance programs following the 2003 WNV outbreak had minimal effect on observations on Aedes spread.therefore may not provide a better time period to mirror the spread of the species in the United States.2.5. Classifying the ranges of each mosquito species and incorporating uncertaintyCurrent reported distributions of Ae. aegypti and Ae. albopictus are unlikely to be fully representative of their actual distributions because of logistical and financial constraints on vector surveillance.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1756-3305-6-209","ISBN":"9789291933785","ISSN":"15607917","PMID":"23866915","abstract":"BACKGROUND: The recent notifications of autochthonous cases of dengue and chikungunya in Europe prove that the region is vulnerable to these diseases in areas where known mosquito vectors (Aedes albopictus and Aedes aegypti) are present. Strengthening surveillance of these species as well as other invasive container-breeding aedine mosquito species such as Aedes atropalpus, Aedes japonicus, Aedes koreicus and Aedes triseriatus is therefore required. In order to support and harmonize surveillance activities in Europe, the European Centre for Disease Prevention and Control (ECDC) launched the production of 'Guidelines for the surveillance of invasive mosquitoes in Europe'. This article describes these guidelines in the context of the key issues surrounding invasive mosquitoes surveillance in Europe.\\n\\nMETHODS: Based on an open call for tender, ECDC granted a pan-European expert team to write the guidelines draft. It content is founded on published and grey literature, contractor's expert knowledge, as well as appropriate field missions. Entomologists, public health experts and end users from 17 EU/EEA and neighbouring countries contributed to a reviewing and validation process. The final version of the guidelines was edited by ECDC (Additional file 1).\\n\\nRESULTS: The guidelines describe all procedures to be applied for the surveillance of invasive mosquito species. The first part addresses strategic issues and options to be taken by the stakeholders for the decision-making process, according to the aim and scope of surveillance, its organisation and management. As the strategy to be developed needs to be adapted to the local situation, three likely scenarios are proposed. The second part addresses all operational issues and suggests options for the activities to be implemented, i.e. key procedures for field surveillance of invasive mosquito species, methods of identification of these mosquitoes, key and optional procedures for field collection of population parameters, pathogen screening, and environmental parameters. In addition, methods for data management and analysis are recommended, as well as strategies for data dissemination and mapping. Finally, the third part provides information and support for cost estimates of the planned programmes and for the evaluation of the applied surveillance process.\\n\\nCONCLUSION: The 'Guidelines for the surveillance of invasive mosquitoes in Europe' aim at supporting the implementation of tailored surveillance of i…","author":[{"dropping-particle":"","family":"Schaffner","given":"Francis","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bellini","given":"Romeo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Petri?","given":"Du?an","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scholte","given":"Ernst-Jan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zeller","given":"Hervé","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rakotoarivony","given":"Laurence M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & Vectors","id":"ITEM-1","issued":{"date-parts":[["2013"]]},"page":"209","title":"Development of guidelines for the surveillance of invasive mosquitoes in Europe","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁷","plainTextFormattedCitation":"37"},"properties":{"noteIndex":0},"schema":""}37 Therefore we used the following method to estimate the current-day global distribution (realised niche) of each mosquito species by comparing environmental suitability maps with occurrence data. We extracted the predicted environmental suitability value at each of the locations where the mosquito species has been reported, and the value of environmental suitability that encompassed 90% of these reported locations was chosen as the range threshold. Every value above or equal to this threshold was defined as within the range of the mosquito species (Extended Data Fig. 13). This approach assumes that the 10% of occurrences outside of the predicted range represent temporary introductions that do not persist longer than one year and are not representative of the long-term distribution of the species. As there is uncertainty in what proportion of the data are representative of these transient identifications (given that the majority of the data are cross-sectional not longitudinal), we undertook a sensitivity analysis that varied this threshold from 85% to 95%, thereby creating 96 different possible range maps that represent different realisations of the current distribution of each species. In doing so, we capture locations that have the conditions for mosquito presence and where there is potential for onward spread. We did not include international shipping as a contributor to infrequent long-distance importation events between continents since both species are already well established on each continent and therefore new occurrences are more likely to be driven by intra-continental importation pressure.3. Future projections3.1. Projecting environmental and socioeconomic covariatesWe used 17 GCMs to estimate 30 arc-sec images for monthly mean climate data. Extended Data Table 7 provides the designation, origin, references and number of replicate runs for each model. The procedures are described in detail in MarkSim documentation65. For each GCM the baseline monthly climate was derived from the historic runs for temperatures and rainfall, the monthly means were calculated for each GCM for the years 2000 to 2095, and the difference ‘delta’ for each month was calculated by subtracting the specific GCM baseline. The deltas were interpolated from the native GCM pixel (Extended Data Tab. 7) to a one degree by one degree pixel for the globe. The data were pattern scaled to WorldClim 1.0364 for each one degree pixel, RCP, and month. For each variant a fourth order polynomial regression was fitted over the 96 years of data and through the origin at 1985 (1985 being the mean midpoint of the data used in the WorldClim construction) to calculate one output per model per year per scenario. Humidity data were estimated directly at the 30 arc-sec level from dewpoint calculated by the tabular method of LinacreADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/0002-1571(77)90007-3","ISBN":"0002-1571","ISSN":"00021571","PMID":"917","abstract":"The Penman formula for the evaporation rate from a lake is simplified to the following: E0= 700Tm(100-A)+15(T-Td) (80-T)(mm day-1where Tm= T + 0.006h, h is the elevation (metres), T is the mean temperature, A is the latitude (degrees) and Tdis the mean dew-point. Values given by this formula typically differ from measured values by about 0.3 mm day-1for annual means, 0.5 mm day-1for monthly means, 0.9 mm day-1for a week and 1.7 mm day-1for a day. The formula applies over a wide range of climates. Monthly mean values of the term (T - Td) can be obtained either from an empirical table or from the following empirical relationship, provided precipitation is at least 5 mm month-1and (T - Td) is at least 4°C: (T-Td) = 0.0023h+0.37T+0.53R+0.35Rann-10.9°C where R is the mean daily range of temperature and Rannis the difference between the mean temperatures of the hottest and coldest months. Thus the evaporation rate can be estimated simply from values for the elevation, latitude and daily maximum and minimum temperatures. ? 1977.","author":[{"dropping-particle":"","family":"Linacre","given":"Edward T.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Agricultural Meteorology","id":"ITEM-1","issue":"6","issued":{"date-parts":[["1977"]]},"page":"409-424","title":"A simple formula for estimating evaporation rates in various climates, using temperature data alone","type":"article-journal","volume":"18"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶⁹","plainTextFormattedCitation":"69","previouslyFormattedCitation":"⁶⁹"},"properties":{"noteIndex":0},"schema":""}69 and the mean temperature. To fully propagate the variation between the climate models through our predictions we used the outputs of 17 GCM, for all 3 years, and 3 scenarios.Global temperature estimates were converted into temperature suitability for mosquito population persistence (separate metrics for each vector species), hereafter referred to as temperature suitability, using temperature-based mathematical models from Riahi et al44 and Fujino et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1756-3305-7-338","ISSN":"1756-3305","PMID":"25052008","abstract":"BACKGROUND: Dengue is a disease that has undergone significant expansion over the past hundred years. Understanding what factors limit the distribution of transmission can be used to predict current and future limits to further dengue expansion. While not the only factor, temperature plays an important role in defining these limits. Previous attempts to analyse the effect of temperature on the geographic distribution of dengue have not considered its dynamic intra-annual and diurnal change and its cumulative effects on mosquito and virus populations. METHODS: Here we expand an existing modelling framework with new temperature-based relationships to model an index proportional to the basic reproductive number of the dengue virus. This model framework is combined with high spatial and temporal resolution global temperature data to model the effects of temperature on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. RESULTS: Our model predicted areas where temperature is not expected to permit transmission and/or Aedes persistence throughout the year. By reanalysing existing experimental data our analysis indicates that Ae. albopictus, often considered a minor vector of dengue, has comparable rates of virus dissemination to its primary vector, Ae. aegypti, and when the longer lifespan of Ae. albopictus is considered its competence for dengue virus transmission far exceeds that of Ae. aegypti. CONCLUSIONS: These results can be used to analyse the effects of temperature and other contributing factors on the expansion of dengue or its Aedes vectors. Our finding that Ae. albopictus has a greater capacity for dengue transmission than Ae. aegypti is contrary to current explanations for the comparative rarity of dengue transmission in established Ae. albopictus populations. This suggests that the limited capacity of Ae. albopictus to transmit DENV is more dependent on its ecology than vector competence. The recommendations, which we explicitly outlined here, point to clear targets for entomological investigation.","author":[{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"Moritz U","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Messina","given":"Jane P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reiner","given":"Robert C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"Thomas W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"Simon I","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & vectors","id":"ITEM-1","issued":{"date-parts":[["2014","7","22"]]},"page":"338","title":"Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission.","type":"article-journal","volume":"7"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1186/1756-3305-6-351","ISSN":"1756-3305","PMID":"24330720","abstract":"BACKGROUND: The survival of adult female Aedes mosquitoes is a critical component of their ability to transmit pathogens such as dengue viruses. One of the principal determinants of Aedes survival is temperature, which has been associated with seasonal changes in Aedes populations and limits their geographical distribution. The effects of temperature and other sources of mortality have been studied in the field, often via mark-release-recapture experiments, and under controlled conditions in the laboratory. Survival results differ and reconciling predictions between the two settings has been hindered by variable measurements from different experimental protocols, lack of precision in measuring survival of free-ranging mosquitoes, and uncertainty about the role of age-dependent mortality in the field.\n\nMETHODS: Here we apply generalised additive models to data from 351 published adult Ae. aegypti and Ae. albopictus survival experiments in the laboratory to create survival models for each species across their range of viable temperatures. These models are then adjusted to estimate survival at different temperatures in the field using data from 59 Ae. aegypti and Ae. albopictus field survivorship experiments. The uncertainty at each stage of the modelling process is propagated through to provide confidence intervals around our predictions.\n\nRESULTS: Our results indicate that adult Ae. albopictus has higher survival than Ae. aegypti in the laboratory and field, however, Ae. aegypti can tolerate a wider range of temperatures. A full breakdown of survival by age and temperature is given for both species. The differences between laboratory and field models also give insight into the relative contributions to mortality from temperature, other environmental factors, and senescence and over what ranges these factors can be important.\n\nCONCLUSIONS: Our results support the importance of producing site-specific mosquito survival estimates. By including fluctuating temperature regimes, our models provide insight into seasonal patterns of Ae. aegypti and Ae. albopictus population dynamics that may be relevant to seasonal changes in dengue virus transmission. Our models can be integrated with Aedes and dengue modelling efforts to guide and evaluate vector control, better map the distribution of disease and produce early warning systems for dengue epidemics.","author":[{"dropping-particle":"","family":"Brady","given":"Oliver J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johansson","given":"Michael a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guerra","given":"Carlos a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhatt","given":"Samir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Delatte","given":"Hélène","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grech","given":"Marta G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leisnham","given":"Paul T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maciel-de-Freitas","given":"Rafael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Styer","given":"Linda M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"David L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"Thomas W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"Simon I","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Parasites & vectors","id":"ITEM-2","issued":{"date-parts":[["2013","1"]]},"page":"351","title":"Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings.","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"^44,45","plainTextFormattedCitation":"44,45","previouslyFormattedCitation":"^44,45"},"properties":{"noteIndex":0},"schema":""}45. These show the effects of diurnal and seasonal changes in temperatures on the generation time of the mosquito and its resultant effects on the persistence of a population.As a highly anthropophilic mosquito species, the future distribution of the Aedes is likely to depend critically on both environmental and human socioeconomic factors that modify the availability of its habitatADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Messina","given":"Jane P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brady","given":"Oliver J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Golding","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pigott","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraemer","given":"Moritz U.G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"T.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wint","given":"G.R.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"D.L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"S.I.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Reviews Microbiology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2015"]]},"page":"230-9","title":"The many projected futures of dengue.","type":"article-journal","volume":"13"},"uris":[""]}],"mendeley":{"formattedCitation":"⁸","plainTextFormattedCitation":"8","previouslyFormattedCitation":"⁸"},"properties":{"noteIndex":0},"schema":""}8. To incorporate these features, we also modelled the continued process of global urbanisation until 2080 using a probabilistic machine learning algorithm based on the work of Linard et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.apgeog.2013.07.009","ISSN":"01436228","author":[{"dropping-particle":"","family":"Linard","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"Andrew J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gilbert","given":"Marius","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Geography","id":"ITEM-1","issued":{"date-parts":[["2013","10"]]},"page":"23-32","publisher":"Elsevier Ltd","title":"Modelling spatial patterns of urban growth in Africa","type":"article-journal","volume":"44"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁸","plainTextFormattedCitation":"48","previouslyFormattedCitation":"⁴⁸"},"properties":{"noteIndex":0},"schema":""}48. Here we use urban growth rates predicted by the United Nations as a predictor variableADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"United Nations Population Division","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2014"]]},"publisher":"United Nations","publisher-place":"New York","title":"World Urbanization Prospects: The 2014 Revision","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁹","plainTextFormattedCitation":"49","previouslyFormattedCitation":"⁴⁹"},"properties":{"noteIndex":0},"schema":""}49 as well as a range of other covariates as previously described in van Vuuren et alADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.apgeog.2013.07.009","ISSN":"01436228","author":[{"dropping-particle":"","family":"Linard","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatem","given":"Andrew J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gilbert","given":"Marius","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Geography","id":"ITEM-1","issued":{"date-parts":[["2013","10"]]},"page":"23-32","publisher":"Elsevier Ltd","title":"Modelling spatial patterns of urban growth in Africa","type":"article-journal","volume":"44"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁸","plainTextFormattedCitation":"48","previouslyFormattedCitation":"⁴⁸"},"properties":{"noteIndex":0},"schema":""}48.3.2. Projecting future niche of Ae. aegypti and Ae. albopictusAlthough niche shifts might occur over long time-periods, the future effects remains unclear for Ae. aegypti and Ae. albopictus since their expansion from their native rangeADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1111/j.1466-8238.2009.00497.x","ISSN":"1466822X","author":[{"dropping-particle":"","family":"Medley","given":"Kim A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Global Ecology and Biogeography","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2010","1","30"]]},"page":"122-33","title":"Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models","type":"article-journal","volume":"19"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷⁰","plainTextFormattedCitation":"70","previouslyFormattedCitation":"⁷⁰"},"properties":{"noteIndex":0},"schema":""}70. Therefore, we assume niche conservatism, implying that the mosquitoes tend to establish and survive under similar environmental conditions in native and invaded ranges in the futureADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1146/annurev.ecolsys.36.102803.095431","ISBN":"1543592X","ISSN":"1543-592X","abstract":"Niche conservatism is the tendency of species to retain ancestral eco- logical characteristics. In the recent literature, a debate has emerged as to whether niches are conserved. We suggest that simply testing whether niches are conserved is not by itself particularly helpful or interesting and that a more useful focus is on the patterns niche that conservatism may (or may not) create. We focus specifically on how niche conservatism in climatic tolerances may limit geographic range expansion and how this one type of niche conservatism may be important in (a) allopatric speciation, (b) historical biogeography, (c) patterns of species richness, (d) community structure, (e) the spread of invasive, human-introduced species, (f) responses of species to global climate change, and (g) human history, from 13,000 years ago to the present. 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However, this assumption has been challenged recently by evidence of niche shifts in some species. Here, we report the first large-scale test of niche conservatism for 50 terrestrial plant invaders between Eurasia, North America, and Australia. We show that when analog climates are compared between regions, fewer than 15% of species have more than 10% of their invaded distribution outside their native climatic niche. These findings reveal that substantial niche shifts are rare in terrestrial plant invaders, providing support for an appropriate use of ecological niche models for the prediction of both biological invasions and responses to climate change.","author":[{"dropping-particle":"","family":"Petitpierre","given":"B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kueffer","given":"C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Broennimann","given":"O.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Randin","given":"C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Daehler","given":"C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guisan","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Science","id":"ITEM-2","issue":"6104","issued":{"date-parts":[["2012"]]},"page":"1344-8","title":"Climatic Niche Shifts Are Rare Among Terrestrial Plant Invaders","type":"article-journal","volume":"338"},"uris":[""]},{"id":"ITEM-3","itemData":{"author":[{"dropping-particle":"","family":"Kraemer","given":"M. 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Each of these 18 maps were composed of 100 ensemble predictions that randomly sampled (with replacement) the following aspects of the analysis:The fitted Aedes BRT model (from a choice of 100 BRT models fitted to 2015 data)The predicted temperature suitability for Aedes survival (from a choice of 17 GCMs)The predicted minimum precipitation (from a choice of 17 GCMs)The predicted relative humidity (from a choice of 17 GCMs)The predicted minimum precipitation (from a choice of 17 GCMs)The predicted geographic expansion via land from the spread models (section 3.3).This approach sought to fully propagate the uncertainty in the climate, Aedes temperature suitability and Aedes models through to the final prediction. These 100 predictions were then summarised by mean and 95% credible intervals to give the final prediction for each year RCP combination. Uncertainties are shown in all maps along the X-axes.Our baseline map modelling is different from previously published maps in so far that it uses only projectable environmental and socio-demographic variables and does not use the Enhanced Vegetation Index (EVI), as the EVI is a direct empirical measure of the Earth’s current greennessADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Kraemer","given":"M. U. 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To minimise potential reduction in the predictive ability of the model by omitting this covariate, we include precipitation and relative humidity as predictors for suitability for green vegetation growth in both the present day and future models.3.3. Projecting mosquito spreadTo derive yearly model-based estimates of the possible expansion of both species by 2080 we forward-simulated the geographic spread model based on the equation in 2.4. To account for the spatio-temporal dependence in first detection probabilities (each district’s probability is a function of every other district that was infested the year before), we run 1,000 simulations forward in time. Within each simulation we estimate the probability of infestation to each district that had yet to detect the species. We then drew a Bernoulli random variable with that probability of ‘1’ (i.e., invasion) and imputed those results for each potential detection. Using these imputed invasions as well as all districts that had previously been infested, we repeat the estimation of range expansion for the next year. This process is repeated up to the desired forecast horizon. This represents a single simulation. It is important to note that we did not allow for the situation where an already infested district will ‘lose’ its infection status (i.e., if xit-1=1 for district i, we force xit=1). We then combine the results of the 1,000 simulations to identify which districts were most likely to have a positive species presence at any point.3.4. Calculating population at risk and area expansionTo classify areas as at risk or not at risk of Ae. aegypti and Ae. albopictus a threshold was defined for the continuous Aedes suitability maps by the value that maximised sensitivity and specificity when classifying the occurrence and background data using the 2015 map. This value was found to be 0.47 and 0.51 for Ae. aegypti and Ae. albopictus respectively. Any pixel with a predicted suitability value above that was considered at risk and the same threshold was applied to each time point and scenario to calculate the population and area at risk in each global region. The final maps for 2020, 2050, 2080 are then overlaid with contemporary estimates of human populations at 5 km resolution and extracted the relevant population at risk was estimated using the raster package in R. We paired the climatic scenarios based on Shared Socioeconomic Pathways (SSPs) that were defined by O’Neill?et al.?in 2014ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s10584-013-0905-2","author":[{"dropping-particle":"","family":"Neill","given":"Brian C O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kriegler","given":"Elmar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Riahi","given":"Keywan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ebi","given":"Kristie L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hallegatte","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carter","given":"T.R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mathur","given":"R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vuuren","given":"D.P.","non-dropping-particle":"van","parse-names":false,"suffix":""}],"container-title":"Climatic Change","id":"ITEM-1","issued":{"date-parts":[["2014"]]},"page":"387-400","title":"A new scenario framework for climate change research : the concept of shared socioeconomic pathways","type":"article-journal","volume":"122"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷³","plainTextFormattedCitation":"73","previouslyFormattedCitation":"⁷³"},"properties":{"noteIndex":0},"schema":""}73. They represent reference pathways that describe plausible alternate trends in the evolution of society and ecosystems over a century, in the absence of climate change or climate policies. SSPs are predicated on possible outcomes that would make it more or less difficult to respond to climate change challenges.?Each SSP consists of quantified population and Gross Domestic Product (GDP) trajectories, serving as the starting points for various organisations to model these factors and to provide projections for demographic and economic development variables. The Integrated Assessment Modelling Consortium (IAMC) made available certain peer-reviewed projections via the International Institute for Applied Systems Analysis (IIASA, ), whereby the SSP storylines were converted into population and GDP projections for 195 countriesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s11111-014-0205-4","author":[{"dropping-particle":"","family":"Kc","given":"Samir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lutz","given":"Wolfgang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Population and Environment","id":"ITEM-1","issued":{"date-parts":[["2014"]]},"page":"243-260","title":"Demographic scenarios by age, sex and education","type":"article-journal","volume":"35"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷⁴","plainTextFormattedCitation":"74","previouslyFormattedCitation":"⁷⁴"},"properties":{"noteIndex":0},"schema":""}74 for every decade between the years 2010 and 2100.ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1.Nsoesie, E. 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Estimates of speed of spread in km/year are based on thin spline regression on mosquito observations since their earliest detection in each continent. Red indicates fast dispersal (km/year) whereas yellow and white indicate slower spread (km/year) velocity (see legend below panel b). Areas highlighted in grey have no reported mosquito presence. Panels d – f summarise the speed of dispersal of Ae. albopictus and Ae. aegypti spread in the United States (d, e) and of Ae. albopictus in Europe (f) starting from their date of first detection until 2017. The red line indicates the average velocity per year across all districts using the thin spline regression model.Fig. 2: Predicted future spread of Aedes aegypti and Aedes albopictus in the United States, estimated using human-mobility metrics and ecological determinants fitted to past occurrence data. Panel A shows the forecasted change in the distribution of Ae. aegypti between 2020 and 2050 using the medium climatic scenario Representative Concentration Pathways 6.0 at the United States county level ranging from -0.25 (blue) to 0.25 (red). Red indicates expansion and dark blue contraction of the Aedes range distribution between 2020 and 2050. Panel b shows the predicted suitability of presence of Ae. aegypti in 2050. Pixels with no predicted suitability are coloured in grey. Panels c and d show the corresponding results for Ae. albopictus.Fig. 3: Predicted future spread of Aedes albopictus in Europe. Panel a shows the expansion (red) and contraction (blue) of Ae. albopictus between 2020 and 2050 under the medium climate scenario RCP6.0 with emissions peaking in 2080. Panel b shows the predicted distribution of Ae. albopictus. Panel b shows the predicted suitability of presence of Ae. albopictus in 2050. Pixels with no predicted suitability are coloured in grey.Fig. 4: Predicted global geographic distribution of Ae. aegypti (a) and Ae. albopictus (c) in 2050 under the medium climatic scenario RCP6.0 and uncertainty for Ae. aegypti (b) and Ae. albopictus (e). Predicted suitability of Ae. aegypti quantile cutoff points were 0.24, 0.66, 0.88. Relative uncertainty was computed as the ratio of the 95% uncertainty intervals and predicted Ae. aegypti suitability for each pixel. Cutoff points for uncertainty were 0.08, 0.18, 0.31. The lowest quantile of predicted suitability is shown in white, and the highest in dark pink. The lowest quantile for uncertainty is white and the highest is blue. The colours overlap such that areas coloured purple have both high predicted suitability of Ae. aegypti and high relative uncertainty. Pixels with no predicted suitability are coloured in grey. Panel c show the corresponding results for Ae. albopictus. Predicted suitability of Ae. albopictus quantile cutoff points were 0.13, 0.41, 0.70. Cutoff points for uncertainty for Ae. albopictus were 0.16, 0.36, 0.53. The global population predicted to live in areas suitable for Ae. aegypti (b) and Ae. albopictus (c) under the conservative (RCP4.5), medium (RCP6.0), and worst-case scenario (RCP8.5) using the binary cutoff values of suitability of 0.46 and 0.51 for both species respectively.Acknowledgements: We want to thank Sarah Ray for editorial review. MUGK acknowledges funding from the Society in Science, The Branco Weiss Fellowship, administered by the ETH Zurich. MUGK also acknowledges funding from the Training Grant from the National Institute of Child Health and Human Development (T32HD040128). MUGK and SIH acknowledge funding from the International Research Consortium on Dengue Risk Assessment Management and Surveillance (IDAMS; European Commission 7th Framework Programme #21893). SIH received a grant from the Research for Health in Humanitarian Crises (R2HC) Programme, managed by ELRHA (#13468) which also supported MUGK and NG. The R2HC programme aims to improve health outcomes by strengthening the evidence base for public health interventions in humanitarian crises. The ?8 million R2HC programme is funded equally by the Wellcome Trust and Department of International Development (DFiD), with Enhancing Learning and Research for Humanitarian Assistance (ELRHA) overseeing the programme’s execution and management. SIH was also funded by a Senior Research Fellowship from the Wellcome Trust (#95066) and grants from the Bill & Melinda Gates Foundation (OPP1106023, OPP1093011, OPP1132415 and OPP1159934). This study was made possible by the support of the American people through the United States Agency for International Development Emerging Pandemic Threats Program-2 PREDICT-2 (Cooperative Agreement number AID-OAA-A-14-00102), which also supported MUGK. JSB is supported by the National Library of Medicine of the National Institutes of Health (R01LM010812, R01LM011965), which also supports MUGK. DLS is funded by the National Institutes of Health and National Institute of Allergy and Infectious Diseases (#U10AI089674). HHN was funded by the European Commission through the European Research Council Advanced Investigator Grant `Momentum' 324247. LL received funding from the French Government's Investissement d'Avenir program, Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases (grant ANR-10-LABX-62-IBEID), the French Agence Nationale de la Recherche (grant ANR-16-CE35-0004), the City of Paris Emergence(s) programme in Biomedical Research, and the European Union’s Horizon 2020 research and innovation programme under ZikaPLAN grant agreement No 734584. NG is supported by a University of Melbourne McKenzie fellowship. WVB, GH, and FS acknowledge funding from VBORNET and VectorNet, an ECDC and EFSA funded project (#ECDC/09/018 and OC/EFSA/AHAW/2013/02), and thank all contributing VBORNET/VectorNet experts for data sharing. TWS, RCR, and LL received funding from the National Institutes of Health Program Project grant (#P01 AI098670). XL is supported by the Natural Science Foundation of China (71522014, 71725001, 71690233 and 71731009).Author Contributions: All contributions are listed in order of authorship. Designed the experiments: MUGK, RCR, OJB, SIH, NG; Provided data: SL, XL, PJ, LB, EW, AJT, GEC, RGC, WVB, GH, FS, CGM, HY, QL; Analyzed the data: MUGK, RCR, OJB, JPM, MG; Interpreted the results: MUGK, RCR, OJB, JPM, MG, DY, DB, TAP, HHN, DLS, LL, SC, NRF, OGP, TWS, GRWW, SIH, NG; Edited the manuscript: JPM, LBM, NDW, SS, GRWW, SIH; Wrote the manuscript: MUGK, OJB, OGP, SIH, NG; All authors read and approved the content of the manuscript.Conflicts of Interest: We declare no competing financial interests.Reprints and permissions information is available at reprintsCorrespondence and requests for materials should be addressed to moritz.kraemer@zoo.ox.ac.uk ................
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