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The Use of Health Geography Modeling to Understand Early Dispersion of COVID-19 in São Paulo, Brazil

Overview
Journal PLoS One
Date 2021 Jan 7
PMID 33411768
Citations 16
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Abstract

Public health policies to contain the spread of COVID-19 rely mainly on non-pharmacological measures. Those measures, especially social distancing, are a challenge for developing countries, such as Brazil. In São Paulo, the most populous state in Brazil (45 million inhabitants), most COVID-19 cases up to April 18th were reported in the Capital and metropolitan area. However, the inner municipalities, where 20 million people live, are also at risk. As governmental authorities discuss the loosening of measures for restricting population mobility, it is urgent to analyze the routes of dispersion of COVID-19 in São Paulo territory. We hypothesize that urban hierarchy is the main responsible for the disease spreading, and we identify the hotspots and the main routes of virus movement from the metropolis to the inner state. In this ecological study, we use geographic models of population mobility to check for patterns for the spread of SARS-CoV-2 infection. We identify two patterns based on surveillance data: one by contiguous diffusion from the capital metropolitan area, and the other hierarchical with long-distance spread through major highways that connects São Paulo city with cities of regional relevance. This knowledge can provide real-time responses to support public health strategies, optimizing the use of resources in order to minimize disease impact on population and economy.

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References
1.
Bennett B, Carney T . Public Health Emergencies of International Concern: Global, Regional, and Local Responses to Risk. Med Law Rev. 2017; 25(2):223-239. PMC: 7107666. DOI: 10.1093/medlaw/fwx004. View

2.
Van Bavel J, Baicker K, Boggio P, Capraro V, Cichocka A, Cikara M . Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav. 2020; 4(5):460-471. DOI: 10.1038/s41562-020-0884-z. View

3.
Kuo C, Fukui H . Geographical structures and the cholera epidemic in modern Japan: Fukushima prefecture in 1882 and 1895. Int J Health Geogr. 2007; 6:25. PMC: 1941729. DOI: 10.1186/1476-072X-6-25. View

4.
Grotto R, Lima R, Berg de Almeida G, Pio Ferreira C, Guimaraes R, Pronunciate M . Increasing molecular diagnostic capacity and COVID-19 incidence in Brazil. Epidemiol Infect. 2020; 148:e178. PMC: 7477464. DOI: 10.1017/S0950268820001818. View

5.
Huang R, Liu M, Ding Y . Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. J Infect Dev Ctries. 2020; 14(3):246-253. DOI: 10.3855/jidc.12585. View