Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil

• Published in: The Science of the total environment Vol. 729; p. 138862
• Main Authors: Prata, David N., Rodrigues, Waldecy, Bermejo, Paulo H.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 10.08.2020
• Subjects: Betacoronavirus; Brazil; capital; Cities; Coronavirus Infections; COVID-19; COVID-19 infection; dose response; Generalized additive model; governance; growth curves; health services; Humans; Pandemics; Pneumonia, Viral; public health; regression analysis; SARS-CoV-2; Temperature; Transmission; Tropical temperature; tropics; viruses; Brazil; Cities; COVID-19; Brazil; Tropical temperature; Generalized additive model; Transmission
• DOI: 10.1016/j.scitotenv.2020.138862
• ISSN: 0048-9697; 1879-1026

The coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue. The novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus. Several studies have robustly identified a relationship between temperature and the number of cases. However, there is no specific study for a tropical climate such as Brazil. This work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil. Cumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19. A generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases. Also, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil. The GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 °C to 27.4 °C. Each 1 °C rise of temperature was associated with a −4.8951% (t = −2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19. A sensitivity analysis assessed the robustness of the results of the model. The predicted R-squared of the polynomial linear regression model was 0.81053. In this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 °C to 27.4 °C. Results indicated that temperatures had a negative linear relationship with the number of confirmed cases. The curve flattened at a threshold of 25.8 °C. There is no evidence supporting that the curve declined for temperatures above 25.8 °C. The study had the goal of supporting governance for healthcare policymakers. [Display omitted] •The dose-response relationships suggest that the relationship between the annual average of temperature compensation and COVID-19 confirmed cases was approximately linear in the range of less than 25.8°C, which became flat above 25.8°C.•When the average temperature was below 25.8°C, each 1°C rise was associated with a −4.8951% (t = −2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.•There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, in temperatures is above 25.8°C.•The polynomial (cubic) regression model can give insights to other researchers for testing new factors and revealing new determinants capable of fitting the trend regression to a maximum of R-squared in COVID-19 cases.