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COVID-19 spatialization by empirical Bayesian model in São Paulo, Brazil
Ivan Vanderley-Silva1, Roberta Averna Valente2
1Program in Planning and Use of Renewable Resources (PPGPUR), Federal University of São Carlos (UFSCAR-Sorocaba), João Leme Dos Santos, Highway (SP-264), Km 110, Sorocaba, SP Brazil.
Geojournal
|November 7, 2022
Summary
This study mapped COVID-19 (Coronavirus Disease) spread in Brazil, finding weak links between high transmission areas and the elderly population. The developed spatial model aids in controlling outbreaks and preventing future diseases.
Area of Science:
- Epidemiology
- Public Health
- Spatial Analysis
Background:
- The global impact of Coronavirus Disease (COVID-19) necessitates understanding its transmission dynamics.
- Identifying high-risk areas is crucial for effective public health interventions.
- Spatial analysis offers a method to visualize and analyze disease spread patterns.
Purpose of the Study:
- To spatialize COVID-19 infections in a municipality near Sao Paulo, Brazil.
- To identify areas with high transmissibility.
- To investigate the association between high transmission areas and the elderly population.
Main Methods:
- Utilized official COVID-19 data.
- Employed an empirical Bayesian model for spatial analysis.
- Mapped infected individuals by region, including older adults.
Main Results:
- Successfully spatialized COVID-19 infections with reasonable model adjustment.
- Revealed a weak correlation between infected regions and the elderly population.
- Demonstrated the utility of spatialization for public health decision-making.
Conclusions:
- A robust spatial model was developed for COVID-19 control.
- The findings support targeted interventions based on transmission hotspots.
- The methodology can be adapted for spatializing and preventing other infectious diseases.
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