How Climate Variables Influence the Spread of SARS-CoV-19 in the United States

André de Souza Melo, Ana Iza Gomes da Penha Sobral, Marcelo Luiz Monteiro Marinho, Gisleia Benini Duarte, Thiago Henrique Ferreira Gomes and Marcos Felipe Falcão Sobral
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André de Souza Melo: Programa de Pós-Graduação em Administração e Desenvolvimento, Federal Rural University of Pernambuco, Rua Dom Manoel de Medeiros, SN, Recife 51171-900, PE, Brazil
Ana Iza Gomes da Penha Sobral: The Cognitive Psychology Department, Federal University of Pernambuco, Recife 50670-901, PE, Brazil
Marcelo Luiz Monteiro Marinho: Programa de Pós-Graduação em Administração e Desenvolvimento, Federal Rural University of Pernambuco, Rua Dom Manoel de Medeiros, SN, Recife 51171-900, PE, Brazil
Gisleia Benini Duarte: Programa de Pós-Graduação em Administração e Desenvolvimento, Federal Rural University of Pernambuco, Rua Dom Manoel de Medeiros, SN, Recife 51171-900, PE, Brazil
Thiago Henrique Ferreira Gomes: Programa de Pós-Graduação em Administração e Desenvolvimento, Federal Rural University of Pernambuco, Rua Dom Manoel de Medeiros, SN, Recife 51171-900, PE, Brazil
Marcos Felipe Falcão Sobral: Programa de Pós-Graduação em Administração e Desenvolvimento, Federal Rural University of Pernambuco, Rua Dom Manoel de Medeiros, SN, Recife 51171-900, PE, Brazil

Sustainability, 2020, vol. 12, issue 21, 1-9

Abstract: During the 2020 Coronavirus pandemic, several scientific types of research investigated the causes of high transmissibility and deaths caused by SARS-CoV-2. Among the spreading factors of the disease, it is known that there is an association between temperature and infected people. However, the studies that identified this phenomenon explored an association relationship, which is weaker and does not allow the identification of which variable would be the cause. This study aimed to analyze the impact of temperature variations and other climatic variables on the infection rate of COVID-19. Data were extracted from weather stations in the United States, which were segregated by county and day. Daily COVID-19 infections and deaths per county were also collected. Two models were used: the first model to analyze the temperature and the number of infected cases and the second model to evaluate the variables of temperature, precipitation, and snow in relation to COVID-19 infection. Model 1 shows that an increase in temperature at time zero caused a decrease in the number of infected cases. Meanwhile, a decrease in temperature after the temperature shock was associated with an increase in the number of cases, which tended to zero overall. A 1% increase in temperature caused a 0.002% decrease in the number of cases. The results suggested a causal relationship between the average temperature and number of CODIV-19 cases. Model 2, which includes temperature, precipitation, and snow shows that an increase in temperature resulted in a 0.00154% decrease response. There was no significant effect of increased precipitation and snow on the infection rate with COVID-19.

Keywords: SARS-CoV-2; temperature; precipitation; snowfall (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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