Evolving Drivers of Brazilian SARS‐CoV‐2 Transmission: A Spatiotemporally Disaggregated Time Series Analysis of Meteorology, Policy, and Human Mobility

Abstract

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has ravaged Brazil. As of July 2022, the country had recorded the second-highest number of cases and second-highest number of deaths globally (Center for Systems Science and Engineering, 2022). Disinformation sowed by Brazilian politicians; the defense of ineffective treatment based on chloroquine; less restrictive social isolation measures in some states and municipalities, especially those aligned with the federal government; difficulty in controlling

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic

“…The dataset is distributed in accessible formats, and optimized for machine learning applications to support reproducible research of high quality. The availability of this dataset has facilitated analyses of COVID-19 risk factors at subnational resolution across multiple countries 15 – 18 and studies of changes in risk factors over the course of the pandemic 19 .…”

Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas

“…The spread of COVID-19 is influenced not only by factors such as population density, individual lifestyle habits and mobility, specific restrictions imposed by governments, vaccination rates, and individual susceptibility, but also by meteorological factors [ [4] , [5] , [6] ]. The relationship between the spread of epidemics and meteorological factors has drawn significant interest from specialists and scholars in the past [ [7] , [8] , [9] , [10] , [11] , [12] ].…”

“…Other studies have failed to correct for the discrepancies that existed in the data reported from different regions, all of which may lead to uncertainty in the analysis results. Recently, an increasing number of studies have used non-linear time series analysis methods, such as GAM [ 5 , 23 , [38] , [39] , [40] , [41] , [42] , [43] , [44] ] and DLNM [ 28 , [44] , [45] , [46] ]. However, these studies encompass analyses at the country [ 43 , 44 ], state [ 5 , 26 , 40 ] or city [ 23 , 28 , 38 , 39 , 41 , 45 , 46 ] level.…”

“…Recently, an increasing number of studies have used non-linear time series analysis methods, such as GAM [ 5 , 23 , [38] , [39] , [40] , [41] , [42] , [43] , [44] ] and DLNM [ 28 , [44] , [45] , [46] ]. However, these studies encompass analyses at the country [ 43 , 44 ], state [ 5 , 26 , 40 ] or city [ 23 , 28 , 38 , 39 , 41 , 45 , 46 ] level. In studies with a country or state as the unit of analysis, the regional scope is relatively large, and acquiring data on meteorological indicators and their representativeness is prone to significant errors.…”