key: cord-1014400-147yc66p authors: Prata, David N.; Rodrigues, Waldecy; Bermejo, Paulo H. title: Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil date: 2020-04-25 journal: Sci Total Environ DOI: 10.1016/j.scitotenv.2020.138862 sha: cc7a5fcd4ce8ced4b5005d4ea8d09da2fcdf9f0a doc_id: 1014400 cord_uid: 147yc66p Abstract 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. COVID 19 is a respiratory epidemic caused by the coronavirus family (2019-nCoV). The disease may cause the rapid death of those affected, depending on their general health conditions and socio-demographic profile. A relevant question about the spread of 2019-nCoV is whether climatic or demographic characteristics enable a more significant expansion of the virus. Bukhari and Jameel (Bukhari et al., 2020) reported that 2019-nCoV spread rapidly to many countries, and a world pandemic crisis was subsequently declared by the World Health Organization (WHO). The authors observed that a similar variant of 2019-nCoV, the influenza virus, has been affected by climate. Comparable to COVID-19, the SARS-associated coronavirus (SARS-CoV) may cause severe acute respiratory syndrome (SARS) . It has also been suggested that SARS-CoV-2 and other closely related coronaviruses, such as influenza and Ebola, had significant relations to environmental factors (Yip et al., 2007; Thai et al., 2015; Ng et al., 2014; Lowen et al., 1982; Bi et al., 2007; Barreca et al., 2012; Moriyama et al., 2019; Casanova et al., 2010) . Some studies (Chan et al., 2011; Van Doremalen, 2013; Hastie, 1990) have claimed that the survival time of coronaviruses on surfaces depends on temperature increases or decreases; therefore, temperature could affect the virus transmission risk. Thus far, it is reasonable to hypothesize that differences in annual average temperatures could significantly influence the transmission of the virus. The prevalence of studies on the relationship between virus transmission and temperature have been conducted for non-tropical countries (30° N and above) in temperatures ranging from −20°C to a maximum of 20°C. Brazil is an expansive, tropical country with most of its territory located between the Tropic of Cancer in the north (about 23° 26′ N) to the Tropic of Capricorn in the south (about 23° 26′ S). Brazil's geographic range makes it possible to compare specific sub-regions called hot J o u r n a l P r e -p r o o f and torrid with temperate climate zones to examine the spread of 2019-nCoV in different climates and territories. This study aims to investigate the role of Brazilian tropical weather in the transmission of coronavirus by exploring the relationship between annual average temperatures and confirmed COVID-19 cases for the state capital cities. The study included 27 cities, all state capitals of Brazil, covering longitudes from 34° 51′ 40″ W to 67° 48′ 27″ W and latitude from 8° 45′ 43″ N to 30° 1′ 40″ S. Figure 1 shows the Koppen climate types of Brazil (adapted by the authors). In Brazil, 93% of the landmass is in the Southern Hemisphere, and the remainder (7%) is in the Northern Hemisphere. This means that the territory is in the tropical zone of the planet, except for the southern region, below the Tropic of Capricorn, corresponding to 6.76% of the Brazilian territory. The study population is the daily number of cumulative confirmed cases of in the 27 state capital cities, as officially reported by the Ministry of Health of Brazil from February 27 to April 1, 2020. This work focused on the capital cities because of the land cover of Brazilian territory and the few cases to date in the interior of the A descriptive analysis was performed, with numerical variables described using means, standard deviations, and distributions. A generalized additive model (GAM) was used to calculate the relationships between the temperature data and the number of cumulative total confirmed cases (lgN), respectively, to fit equations and splines. GAM fits generalized additive models Wu et al., 2018) for parametric and nonparametric regression and smoothing. GAM can be useful to explore linear and nonlinear weather effects and health outcomes . The model defined is semiparametric and additive, as follows: The model attempts to represent the polynomial behavior of the growth curve of the cumulative confirmed cases of the state capital cities of Brazil. The log-transformed daily cumulative COVID-19 counts in capital city i on day t. is the intercept, (•) denotes a spline function with a maximum of two degrees of freedom to avoid overfitting Wang et al., 2018) , β is the parameter of x, and x is the linear variable countdays in capital city i on day t. The variable countdays is the counting days since the first outbreak in city i. The annual average of temperatures compensation , the demographic density , and the estimated population were controlled for confounding effects. Two tests for measuring the robustness of the sensitivity of the model were applied. First, the São Paulo capital city was removed from the data for two reasons: (1) it is by far the largest state capital city of Brazil, with almost the double the population of the J o u r n a l P r e -p r o o f second-most populated state capital, and (2) the total confirmed COVID-19 cases for São Paulo is more than three times the quantity of cases than the second-highest city. In the sensitivity analysis, the second test considered the logN of confirmed cases of COVID-19 per habitant for two reasons. First, the test accounted for the proportion of the population in the confirmed cases, and second, because a significantly negative correlation between temperature and population was found. A hypothesis, therefore, arises whether the significantly negative correlation between the COVID-19 confirmed cases and temperature is due to the greater size of the population in the cities with low temperatures. The GAM model was built in SAS, with two-sided tests, and p < 0.05 was considered statistically significant. The dose-response relationship in Figure Although the decline of the curve stopped at 25.8°C, Table 3 shows that for each 1°C rise in temperature, there is a decrease in the cumulative daily number of COVID-19 confirmed cases. The percentage of this decrease depends on factoring in the population per habitant and removing the São Paulo city data. The dose-response relationship was robust to the findings of the sensitivity analysis. Linear(estimated_population_2019), estimate 7.752899E-8, t-value 6.06, p < .0001. Thus, the linear model can be defined as follow: The parameters are shown in Table 4 . The model is an attempt to represent the behavior of the growth curve of COVID-19 for the state capital cities of Brazil. Figure 3 shows the growth curve of COVID-19 confirmed cases and predicted values. The individual Rsquared of the factors was also calculated. The adjusted R-squared of the model was 0.81357, R-squared 0.81580, and the predicted R-squared 0.81053. The R-squared of countdays was 0.69311, the adjusted R-squared 0.69152, and the predicted R-squared 0.68924. The R-squared of temperature was 0.095873, the adjusted R-squared 0.094325, and the predicted R-squared was 0.089251. The R-squared of demographic density was 0.17693, the adjusted R-squared 0.17410, and the predicted R-squared was 0.16797. The R-squared of the estimated population was 0.20670, the adjusted Rsquared was 0.20534, and the predicted R-squared was 0.19865. Table 5 summarizes the R-squared results. Findings suggest that these factors have a reasonable contribution to explaining the behavior of the COVID-19, at least for this linear model. The strong motivation for this work was the lack of a study of COVID-19 in tropical climate countries, to the authors' knowledge. To achieve this purpose, we explored This result suggests that temperature may be explored in nonlinear models (Wu et al., 2018) . In our data, the first counting days of the outbreak had few cases reported. This may be because the outbreak was just beginning, and there was a natural lack of COVID-19 tests, and also because of health infrastructure deficiencies when the first virus contact occurred. Managing data, such as creating a mean value of the case numbers for the three or four days following the first diagnosed case, appears to be a sound strategy for data analysis, as used in other works . The counting days also could help to abstract data, allowing for a timeless analysis by placing all data in the same time of the counting days. Previous studies (Bukhari et al., 2020) have shown significant relationships between cooler and warmer-humid regions. Thus, warmer-humidity is another interesting factor to investigate in tropical climate zones. Many studies Yongjiana et al., 2020; Núñez-Delgado, 2020; Liu et al., 2020; Yip et al., 2007; Thai et al., 2015; Ng et al., 2014; J o u r n a l P r e -p r o o f Lowen et al., 1982; Bi et al., 2007; Barreca et al., 2012; Moriyama et al., 2019; Casanova et al., 2010; Wang et al., 2018; Xu et al., 2020) observed temperature conditions beneficial to the coronavirus. Research also revealed that an elevated temperature was harmful to the virus (Bi et al., 2007; Casanova et al., 2010; Chan et al., 2011; Van Doremalen et al., 2013) . However, in this study, we could not evince a negative effect on COVID-19 infection for higher temperatures above 25°C. A likely reason may be the lack of quantitative data to explore, or perhaps that COVID-19 could, in fact, fit these higher temperatures. Further studies need to be conducted to discover new findings and determinants. To our knowledge, these study findings show that COVID-19 may not vanish by itself because the weather becomes warmer. We stress that the governance of healthcare public policies cannot wait for higher temperatures to defeat COVID-19. After all, the efficient adoption of social distance policies by the Brazilian governments was an improvement in the prevention and obstruction of the viral infection. Notably, social distancing may have had a direct impact on these research results because of the change in the natural behavior of the virus. This study intends to contribute to community research with a polynomial linear regression model (cubic) that attempts to represent the behavior of the COVID-19 growth curve. A polynomial linear model fitted well for the state capital cities of Brazil. The results were in agreement with published studies Le et al., 2020; Bukhari et al., 2020; Yongjiana et al., 2020; Núñez-Delgado, 2020; Liu et al., 2020) J o u r n a l P r e -p r o o f Koppen's climate classification map for Brazil Absolute humidity, temperature, and influenza mortality: 30 years of county-level evidence from the United States Weather: driving force behind the transmission of severe acute respiratory syndrome in China? 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The Lancet Possible meteorological influence on the severe acute respiratory syndrome (SARS) community outbreak at Amoy Gardens, Hong Kong Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Science of the Total Environment Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of the Total Environment The authors acknowledge the financial support of the Ministry of Health of Brazil. J o u r n a l P r e -p r o o f