Geographical intelligence applied to public health: the examples of Rio de Janeiro and Niterói municipalities (Rio de Janeiro, Brazil) to face SARS-CoV-2
 
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1
Programa de Pós-Graduação em Geografia da Rio de Janeiro State University (PPGEO/UERJ), Rio de Janeiro, Brazil
2
Rio de Janeiro State University, Rio de Janeiro, Brazil
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Programa de Pós-graduação em Geografia Universidade Federal do Paraná, Brazil
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PPGEO/UERJ, Rio de Janeiro State University, Rio de Janeiro, Brazil
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PPGEO/UFPR, Federal University of Parana, Brazil
Publication date: 2023-04-27
 
Popul. Med. 2023;5(Supplement):A357
 
ABSTRACT
This paper aimed to apply geographical intelligence to analyze the spread of coronavirus, between March 2020 and November 2022, in two municipalities in Rio de Janeiro state: Rio de Janeiro and Niterói, with national strategic and economic importance. Geographers has been produced important contributions in the analysis of geographical elements associated with the dissemination of diseases, taking into account their determinants/conditioners and the analysis of geographical space. To this end, analyzing the spread of diseases goes beyond its spatialization, identifying also its social determinants/conditioners and those territorial technical networks preferred for virus dissemination. Our hypothesis is that, in the absence of clinical control, geographical intelligence had the capacity to anticipate the dynamics of virus diffusion, qualifying the decision-making process by public agents and, later, with vaccination, to evaluate its impact and reorient decision-making, especially in face of growing antivaccine movement. In the first stage were identified patterns in time series data of confirmed cases and deaths with Pettit’s technique and these results was analyzed together with the vaccination schedule, circulation of new strains and public policies measures. The results indicated that the emergence of less lethal strains, such as Delta variant, and vaccine advances were decisive to reduce the populations tendency to become infected. After, Kernel technique was applied to the epidemiological data, revealing the temporal-spatial dynamics of disease spread, which took advantage of the logistical infrastructure and technical network, inequalities in vaccination coverage and deficiencies in the public health system. Finally, contamination scenarios were built up and confirmed the trends of virus diffusion, demonstrating the importance of geographic intelligence for decision making in public health and the health-disease-care process as an object of interest of Geography.
ISSN:2654-1459