Role of Weather Factors in COVID-19 Deaths in Tropical Climate: A Data-Driven Study Focused on Brazil Manuscript
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Abstract
Background
Brazil reported 123,780 deaths across 27 administrative regions, making it the second-worst affected country after the US in terms of COVID-19 deaths as of 3 September 2020. Understanding the role of weather factors in COVID-19 in Brazil is helpful in the longterm mitigation strategy of COVID-19 in other tropical countries because Brazil experienced early large-scale outbreak among tropical countries. Recent COVID-19 studies indicate that relevant weather factors such as temperature, humidity, UV Index (UVI), precipitation, ozone, pollution and cloud cover may influence the spread of COVID-19. Yet, the magnitude and direction of those associations remain inconclusive. Furthermore, there is only limited research exploring the impact of these weather factors in a tropical country like Brazil. In this observational study, we outline the roles of 7 relevant weather factors including temperature, humidity, UVI, precipitation, ozone, pollution (visibility) and cloud cover in COVID-19 deaths in Brazil.
Methods
We use a log-linear fixed-effects model to a panel dataset of 27 administrative regions in Brazil across 182 days (n=3882) and analyze the role of relevant weather factors by using daily cumulative COVID-19 deaths in Brazil as the dependent variable. We carry out robustness checks using case-fatality-rate (CFR) as the dependent variable.
Findings
We control for all time-fixed and various time-varying region-specific factors confounding factors. We observe a significant negative association of COVID-19 daily deaths growth rate in Brazil with weather factors – UVI, temperature, ozone and cloud cover. Specifically, a unit increase in UVI, maximum temperature, and ozone independently associate with 6.0 percentage points [p<0.001], 1.8 percentage points [p<0.01] and 0.3 percentage points [p<0. 1] decline in COVID-19 deaths growth rate. Further, a unit percentage increase in cloud cover associates with a decline of 0.148 percentage points [p<0.05] in COVID-19 deaths growth rate. Surprisingly, contrary to other studies, we do not find evidence of any association between COVID-19 daily deaths growth rate and humidity, visibility and precipitation. We find our results to be consistent even when we use the CFR as the dependent variable.
Interpretation
We find independent protective roles of UVI, temperature, ozone and cloud cover in mitigating COVID-19 deaths, even in a tropical country like Brazil. We observe these results to be consistent across various model specifications, especially for UVI and cloud cover, even after incorporating additional time-varying weather parameters such as dewpoint, pressure, wind speed and wind gust. These results could guide health-related policy decision making in Brazil as well as similar tropical countries.
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SciScore for 10.1101/2020.09.13.20193532: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We then used Python Geocoder to identify the latitude and longitude of these 27 administrative regions of Brazil (26 states, 1 Federal District). Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were …
SciScore for 10.1101/2020.09.13.20193532: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We then used Python Geocoder to identify the latitude and longitude of these 27 administrative regions of Brazil (26 states, 1 Federal District). Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
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