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Methods:Data of confirmed COVID-19 cases was derived from the?Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) ADDIN EN.CITE <EndNote><Cite><Author>Dong</Author><RecNum>18</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>18</rec-number><foreign-keys><key app="EN" db-id="w5v90rat6xp207efwtnxsxsmwvzpzfr0t0xw" timestamp="1584130865">18</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dong, Ensheng</author><author>Du, Hongru</author><author>Gardner, Lauren</author></authors></contributors><titles><title>An interactive web-based dashboard to track COVID-19 in real time</title><secondary-title>The Lancet Infectious Diseases</secondary-title></titles><periodical><full-title>The Lancet Infectious Diseases</full-title></periodical><dates></dates><publisher>Elsevier</publisher><isbn>1473-3099</isbn><urls><related-urls><url>(20)30120-1</url></related-urls></urls><electronic-resource-num>10.1016/S1473-3099(20)30120-1</electronic-resource-num><access-date>2020/03/13</access-date></record></Cite></EndNote>1up to March 19, 2020. Countries with less than 50 diagnosed patients as well as countries not categorized as local transmission according to the WHO situation report as of March 9 were excluded in order to minimize confounding of imported disease transmission. We prospectively defined “warm” and “cold” climate countries according to the following criteria: A “Cold” climate country was defined as a country with average temperatures below 15? Celsius degrees (<77? Fahrenheit) during the month of March ADDIN EN.CITE <EndNote><Cite><Author>Sajadi</Author><Year>2020</Year><RecNum>29</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>29</rec-number><foreign-keys><key app="EN" db-id="w5v90rat6xp207efwtnxsxsmwvzpzfr0t0xw" timestamp="1584140034">29</key></foreign-keys><ref-type name="Online Database">45</ref-type><contributors><authors><author>Sajadi, Mohammad M.</author><author>Habibzadeh, Parham </author><author>Vintzileos, Augustin </author><author>Shokouhi, Shervin </author><author>Miralles-Wilhelm Fernando</author><author>Amoroso Anthony</author></authors></contributors><titles><title> Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19 </title><secondary-title>SSRN</secondary-title></titles><edition>March 5, 2020</edition><dates><year>2020</year></dates><publisher>SSRN</publisher><urls><related-urls><url> </url></related-urls></urls><electronic-resource-num> and latitude line north of 40?. A “Warm” climate country was defined as a country with average March temperatures above 15? Celsius degrees (≥77? Fahrenheit). Based on the assumption that Italy has experienced an unproportionally high local disease spread not essentially affected by weather (in a similar manner to the Wuhan outbreak) we tested our model with and without the addition of Italy. We did not include China in our analysis given the unique circumstances associated with the country being the origin of the outbreak, the lag between outbreak and detection that may confound spread as well as the drastic mitigation steps applied. We assessed disease spread by two measures: The Replication Rate and the Rate of Spread. Replication rate (RR) was defined as the slope of the logarithmic curve of the natural logarithm of the number of cases diagnosed in each country, starting from the day in which the total number of diagnosed cases was ≥30. The choice of 30 for the point we start counting diagnosed cases was chosen as a cut-off based on 2 standard deviations from the mean diagnosed patients in countries with imported cases only (based on WHO situation report at March 5, 2020) We calculated the slope of a sliding window of size (dT), where we chose dT=3. Let Ct be the number of validated cases of COVID-19 for each country at day t.Replication Rate=lnCt+dT-lnCtdT.Rate of Spread (RoS) was calculated based on the method presented by Sajadi et al. ADDIN EN.CITE <EndNote><Cite><Author>Sajadi</Author><Year>2020</Year><RecNum>29</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>29</rec-number><foreign-keys><key app="EN" db-id="w5v90rat6xp207efwtnxsxsmwvzpzfr0t0xw" timestamp="1584140034">29</key></foreign-keys><ref-type name="Online Database">45</ref-type><contributors><authors><author>Sajadi, Mohammad M.</author><author>Habibzadeh, Parham </author><author>Vintzileos, Augustin </author><author>Shokouhi, Shervin </author><author>Miralles-Wilhelm Fernando</author><author>Amoroso Anthony</author></authors></contributors><titles><title> Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19 </title><secondary-title>SSRN</secondary-title></titles><edition>March 5, 2020</edition><dates><year>2020</year></dates><publisher>SSRN</publisher><urls><related-urls><url> </url></related-urls></urls><electronic-resource-num> . It is calculated by running a linear regression of ln(Confirmed Cases) on time, and taking RoS to be the slope coefficient of the regression. We used a 7-day sliding window, as in Sajadi et al. ADDIN EN.CITE <EndNote><Cite><Author>Sajadi</Author><Year>2020</Year><RecNum>29</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>29</rec-number><foreign-keys><key app="EN" db-id="w5v90rat6xp207efwtnxsxsmwvzpzfr0t0xw" timestamp="1584140034">29</key></foreign-keys><ref-type name="Online Database">45</ref-type><contributors><authors><author>Sajadi, Mohammad M.</author><author>Habibzadeh, Parham </author><author>Vintzileos, Augustin </author><author>Shokouhi, Shervin </author><author>Miralles-Wilhelm Fernando</author><author>Amoroso Anthony</author></authors></contributors><titles><title> Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19 </title><secondary-title>SSRN</secondary-title></titles><edition>March 5, 2020</edition><dates><year>2020</year></dates><publisher>SSRN</publisher><urls><related-urls><url> </url></related-urls></urls><electronic-resource-num> . RoSRate of Spread RoSn+7=slope of the linear regression onlnCn,…,ln?(Cn+6)The calculation of the RoS was conducted by using a window for regression that does not include any missing values. From the RoS one can estimate the doubling time of cases: Doubling Time = ln2*1RoS. ADDIN EN.CITE <EndNote><Cite><Author>Park</Author><Year>2019</Year><RecNum>52</RecNum><DisplayText><style face="superscript">3</style></DisplayText><record><rec-number>52</rec-number><foreign-keys><key app="EN" db-id="w5v90rat6xp207efwtnxsxsmwvzpzfr0t0xw" timestamp="1584756258">52</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Park, S. W.</author><author>Champredon, D.</author><author>Weitz, J. S.</author><author>Dushoff, J.</author></authors></contributors><auth-address>Department of Mathematics &amp; Statistics, McMaster University, Hamilton, Ontario, Canada.&#xD;Department of Biology, McMaster University, Hamilton, Ontario, Canada; Department of Mathematics &amp; Statistics, Agent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada.&#xD;School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States; School of Physics, Georgia Institute of Technology, Atlanta, Georgia, United States.&#xD;Department of Biology, McMaster University, Hamilton, Ontario, Canada. Electronic address: dushoff@mcmaster.ca.</auth-address><titles><title>A practical generation-interval-based approach to inferring the strength of epidemics from their speed</title><secondary-title>Epidemics</secondary-title></titles><periodical><full-title>Epidemics</full-title></periodical><pages>12-18</pages><volume>27</volume><edition>2019/02/26</edition><keywords><keyword>*Basic reproduction number</keyword><keyword>*Generation interval</keyword><keyword>*Infectious disease modeling</keyword></keywords><dates><year>2019</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>1878-0067</isbn><accession-num>30799184</accession-num><urls></urls><electronic-resource-num>10.1016/j.epidem.2018.12.002</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>3Databases usedCountry population data was taken from the United Nations website, the Department of Economic and Social Affairs Population ().COVID-19 diagnostic test numbers were taken from updated for March, 20, 2020. Climate data was derived from and based on country capital historical average climate for the month of March (based on weather data at and missing data was added from en.climate-). Average temperature, precipitation in mm, morning and evening humidity, dew point (the temperature to which air must be cooled in order to reach saturation with water) and wind speed (km/h) were collected. All analyses conducted are presented and available at AnalysisContinuous variables are reported as mean ± SEM. Group differences in continuous variables were tested using the Student t-test.?Correlation between weather parameters was conducted using Pearson and Spearman correlation were calculated according to data distribution. A value of P<0.05 was considered statistically significant. Statistical analysis was conducted using GraphPad Prism 6 and R studio gplot2 package. ADDIN EN.REFLIST 1.Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases.2.Sajadi MM, Habibzadeh P, Vintzileos A, Shokouhi S, Fernando M-W, Anthony A. Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19 SSRN. March 5, 2020 ed: SSRN; 2020.3.Park SW, Champredon D, Weitz JS, Dushoff J. A practical generation-interval-based approach to inferring the strength of epidemics from their speed. Epidemics 2019; 27: 12-8.Supplementary Table 1- Population size, COVID-19 tests and replication rate and rate of spread.CountryClimateCategoryReplication rate (Mean±SEM)Rate of Spread(Mean±SEM)Population sizeTestsTests/1000 personsUnited KingdomCold0.25±0.020.25±0.0167886000646210.951905SwitzerlandCold0.27±0.020.27±0.01865461840000.462181NetherlandsCold0.26±0.030.24±0.021713487360000.350163BelgiumCold0.23±0.030.22±0.0111589616183601.584177IcelandCold0.15±0.010.16±0.01341250918926.92747ItalyCold0.23±0.020.23±0.02604618282068863.421762CanadaCold0.21±0.020.2±0.01377421571131212.997205AustriaCold0.28±0.010.3±0.018891383156131.75597FranceCold0.26±0.020.27±0.0164990512367470.565421GermanyCold0.28±0.020.28±0.01831244131670002.009037NorwayCold0.24±0.030.26±0.035337960437358.193205SpainCold0.31±0.020.33±0.0146692863300000.642496SwedenCold0.23±0.030.24±0.039971630143001.434068EgyptWarm0.15±0.020.16±0.02102334403BahrainWarm0.1±0.020.11±0.0115694401864511.88003SingaporeWarm0.06±0.010.05±05850343MalaysiaWarm0.2±0.030.19±0.0232365998138760.428722AustraliaWarm0.18±0.010.17±0.01254998811136154.455511ThailandWarm0.05±0.010.04±0.016979997870840.10149IraqWarm0.11±0.010.11±0.0140222503CountryReplication rate (Mean±SEM)Rate of Spread(Mean±SEM)Temperature (?C)Precipitation(mm)MorningHumidity (%)EveningHumidity(%)Dew point(?C)Wind(km/hr)California0.21±0.010.2212.671.19.198552Colorado0.26±0.050.27±0.024.4337.496739Connecticut0.56?2.99711117251Florida0.36±0.010.3715.8149.97.998849Georgia0.31±0.030.3112.4121.99.7117848Illinois0.32±0.050.285.66610.7128159Louisiana0.36±0.03?16121998652Maryland0.32?710810???Massachusetts0.13±0.010.133.410112126957Michigan0.46±0.15?1.753.311.2148362Minnesota0.21±0.05?0.940.6????Nevada0.2?5.5306???New Jersey0.42±0.04?5.29911???New York0.36±0.040.31±0.021.96911127654North Carolina0.32±0.07?10.9104.19.8108046Ohio0.29?5.576.29147655Oregon0.20±0.04?8.3101.617178862Pennsylvania0.28±0.02?5.283.810.7117049South Carolina0.3?13.1948.6118444Tennessee0.29±0.04?9.812311128051Texas0.28±0.04?1657878047Utah0.24?6.445.710107147Virginia0.19±0.02?9.2101.610.3117846Washington0.17±0.020.17±0.016.913017189161Wisconsin0.38±0.03?1.155.910118159Supplementary Table 2- Climate data, Replication rate and rate of Spread in USA states.Supplementary Table 3- Correlation between replication rate and climate parameters in USA states.Weather ParameterR95% confidence intervalp valueTemp (C?)-0.13-0.50 to 0.280.543Precipitation (mm)0.05-0.35 to 0.440.804Morning Humidity (%)-0.01-0.44 to 0.420.962Evening Humidity (%)0.04-0.40 to 0.460.865Dew point (C?)-0.19-0.57 to 0.270.414Wind (km/h)0.07-0.37 to 0.490.754Supplementary Table 4 - Correlation between rate of spread and climate parameters in USA states.Weather ParameterR95% confidence intervalp valueTemp (C?)0.40-0.42 to 0.860.322Precipitation (mm)0.06-0.67 to 0.730.892Morning Humidity (%)0.12-0.64 to 0.760.779Evening Humidity (%)-0.48-0.89 to 0.340.228Dew point (C?)0.21-0.58 to 0.800.618Wind (km/h)-0.25-0.84 to 0.620.587 ................
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