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10.1016/j.ijid.2020.04.068 http://scihub22266oqcxt.onion/10.1016/j.ijid.2020.04.068 32360939!7192072!32360939     free     free     free       Int+J+Infect+Dis 2020 ; 95 (ä): 301-303 Nephropedia Template TP {{tp|p=32360939|t=2020. Effect of temperature on the infectivity of COVID-19 |pdf=|usr=}}{{32360939}} gab.com Text Effect of temperature on the infectivity of COVID-19 JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32360939 #FOAvip OBJECTIVES: To evaluate the influence of temperature on the infectivity of COVID-19 in Japan. METHODS: We evaluated the relationship between the accumulated number of patients per 1,000,000 population and the average temperature in February 2020 in each prefecture by Poisson regression analysis. We introduced the monthly number of inbound visitors from China in January 2020 in each prefecture and old-age dependency ratio as additional explanatory variables in the model. RESULTS: Monthly inbound visitors from China in January 2020, old-age dependency ratio, and mean temperature in February 2020 are associated with the cumulative number of COVID-19 case on March 16, 2020. CONCLUSIONS: Our analysis showed a possible association between low temperature and increased risk of COVID-19 infection. Further evaluation would be desirable at a global level. Twit Text FOAVip Effect of temperature on the infectivity of COVID-19 ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32360939 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32360939 #FOAvip English Wikipedia <ref>{{Cite pmid|32360939}}</ref> Effect of temperature on the infectivity of COVID-19 #MMPMID32360939 Ujiie M; Tsuzuki S; Ohmagari N Int J Infect Dis 2020[Jun]; 95 (ä): 301-303 PMID32360939show ga OBJECTIVES: To evaluate the influence of temperature on the infectivity of COVID-19 in Japan. METHODS: We evaluated the relationship between the accumulated number of patients per 1,000,000 population and the average temperature in February 2020 in each prefecture by Poisson regression analysis. We introduced the monthly number of inbound visitors from China in January 2020 in each prefecture and old-age dependency ratio as additional explanatory variables in the model. RESULTS: Monthly inbound visitors from China in January 2020, old-age dependency ratio, and mean temperature in February 2020 are associated with the cumulative number of COVID-19 case on March 16, 2020. CONCLUSIONS: Our analysis showed a possible association between low temperature and increased risk of COVID-19 infection. Further evaluation would be desirable at a global level. |*Betacoronavirus[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*epidemiology[MESH] |Female[MESH] |Humans[MESH] |Japan/epidemiology[MESH] |Male[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |Poisson Distribution[MESH] |SARS-CoV-2[MESH]Effect of temperature on the infectivity of COVID-19 (epmc).pdf DeepDyve Pubget Overpricing