Endemismo urbano por COVID-19 en Petrópolis: Detección de un foco endémico mediante análisis espacial

Felix J. Rosenberg; Caiett Genial; Bruno Cesar dos Santos

doi:10.17843/rpmesp.2023.402.11341

Authors

Felix J. Rosenberg Fórum Itaboraí, Fiocruz, Petrópolis, Brasil https://orcid.org/0000-0002-3019-0402
Caiett Genial Fórum Itaboraí, Fiocruz, Petrópolis, Brasil https://orcid.org/0000-0002-9264-9426
Bruno Cesar dos Santos Fórum Itaboraí, Fiocruz, Petrópolis, Brasil https://orcid.org/0000-0002-6388-1082

DOI:

https://doi.org/10.17843/rpmesp.2023.402.11341

Keywords:

SARS-CoV-2, COVID-19, Pandemics, Ecoepidemiology, Disease Hotspot, Spatial Analysis, Brazil

Abstract

This study aimed to identify an ecosystem of urban endemism that explains the persistence of SARSCoV-2 during the first 18 months of the pandemic in the municipality of Petrópolis, Rio de Janeiro, Brazil. We analyzed official records of monthly COVID-19 cases, georeferenced according to the residence address of each confirmed case. Monthly heat maps identifying points with different spatial densities of the disease were constructed by applying the kernel methodology. Heat spots with five intensity levels were identified for the spatial density of cases. The points of highest intensity, known as hotspots, remained constant throughout the period in a polygon of approximately 4 km2 located in the center of the city of Petrópolis. In conclusion, we found that the highest concentration of cases remained in the same location over time, despite the sporadic dispersion of cases within the municipal territory.

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References

Ríos Quituizaca P, Calderón L, Piedra S, Guerrero J, Narváez A. Propuesta de análisis territorial para enfrentar la pandemia por SARS-CoV-2 basado en el perfil de neumonía e influenza en Ecuador 2016-2018 [Proposal for territorial analysis to face the SARS-CoV-2 pandemic based on the Profile of pneumonia and Influenza in Ecuador 2016-2018]. Aten Primaria. 2021 May;53(5):102021. Spanish. doi: 10.1016/j.aprim.2021.102021.

Mas JF, Pérez-Vega A. Spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level. PeerJ. 2021 Dec 24;9:e12685. doi: 10.7717/peerj.12685.

Azevedo L, Pereira MJ, Ribeiro MC, Soares A. Geostatistical COVID-19 infection risk maps for Portugal. Int J Health Geogr. 2020 Jul 6;19(1):25. doi: 10.1186/s12942-020-00221-5.

Scarpone C, Brinkmann ST, Große T, Sonnenwald D, Fuchs M, Walker BB. A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany. Int J Health Geogr. 2020 Aug 13;19(1):32. doi: 10.1186/s12942-020-00225-1.

Fatima M, O’Keefe KJ, Wei W, Arshad S, Gruebner O. Geospatial Analysis of COVID-19: A Scoping Review. Int J Environ Res Public Health. 2021 Feb 27;18(5):2336. doi: 10.3390/ijerph18052336.

Franch-Pardo I, Napoletano BM, Rosete-Verges F, Billa L. Spatial analysis and GIS in the study of COVID-19. A review. Sci Total Environ. 2020 Oct 15;739:140033. doi: 10.1016/j.scitotenv.2020.140033.

Andrade LA, Gomes DS, Góes MAO, Souza MSF, Teixeira DCP, Ribeiro CJN, et al. Surveillance of the first cases of COVID-19 in Sergipe using a prospective spatiotemporal analysis: the spatial dispersion and its public health implications. Rev Soc Bras Med Trop. 2020;53:e20200287. doi: 10.1590/0037-8682-0287-2020.

Saeed U, Sherdil K, Ashraf U, Mohey-Ud-Din G, Younas I, Butt HJ, et al. Identification of potential lockdown areas during COVID-19 transmission in Punjab, Pakistan. Public Health. 2021 Jan;190:42-51. doi: 10.1016/j.puhe.2020.10.026.

Pavlovsky EN. Natural Nidality of Transmissible Diseases. (translation 1966, edited by N. D. Levine, xiv + 261 p., 126 fig). Urbana and London: Univ. of Illinois Press; 1964.

Silva LJ da. O conceito de espaço na epidemiologia das doenças infecciosas. Cad Saúde Pública. 1997;13(4):585-93. doi: 10.1590/S0102-311X1997000400002.

Wolfe ND, Dunavan CP, Diamond J. Origins of major human infectious diseases. Nature. 2007 May 17;447(7142):279-83. doi: 10.1038/nature05775.

Roche B, Broutin H, Choisy M, Godreuil S, de Magny GC, Chevaleyre Y, et al. The niche reduction approach: an opportunity for optimal control of infectious diseases in low-income countries? BMC Public Health. 2014 Jul 25;14:753. doi: 10.1186/1471-2458-14-753.

Rosenberg FJ, Astudillo VM, Goic RM. Regional strategies for the control of foot and mouth disease: an ecological outlook. In: Proc. of the 2nd Int. Symp. on Veterinary Epidemiology and Economics, Canberra: Australian Government Printing Service; 1980. p. 587-596.

Gatrell AC, Bailey TC. Interactive spatial data analysis in medical geography. Soc Sci Med. 1996 Mar;42(6):843-55. doi: 10.1016/0277-9536(95)00183-2.

Câmara G, Carvalho MS. Análise de eventos pontuais. In: Druck S, Carvalho MS, Câmara G, Monteiro AVM. Análise Espacial de Dados

Geográficos. Brasília: EMBRAPA; 2004.

Bergamaschi RB. SIG aplicado a segurança no trânsito: estudo de caso no município de Vitória – ES. Trabalho de Conclusão de Curso. Vitória: Universidade Federal do Espírito Santo; 2010.

Kawamoto MT. Análise de técnicas de distribuição espacial com padrões pontuais e aplicação a dados de acidentes de trânsito e a dados de dengue de Rio Claro–SP [Dissertação mestrado]. Botucatu, SP: Universidade Estadual Paulista, Instituto de Biociências de Botucatu; 2012.

Rizzatti M, Lampert Batista N, Cezar Spode PL, Bouvier Erthal D, Mauro de Faria R, Volpato Scotti AA, et al. Mapeamento da COVID-19 por meio da densidade de Kernel. RMA [Internet]. 12 de junho de 2020 [citado 13 de junho de 2023];3:44-53. Disponível em: https://publicacoes.ifc.edu.br/index.php/metapre/article/view/1312.

Bailey TC, Gatrell AC. Interactive Spatial Data Analysis. London: Longman; 1995. p. 413p.

Menezes PML, Fernandes MC. Roteiro de Cartografia. São Paulo: Oficina de textos; 2013.

Instituto Brasileiro de Geografia e Estatística. Características da população e dos domicílios. Censo demográfico de 2010. Rio de Janeiro: IBGE; 2020 [accesado en 06/10/2021]. Disponible en: https://censo2010.ibge.gov.br/resultados. 2011.

Instituto Brasileiro de Geografia e Estatística. Petrópolis. Cidades e Estados. Rio de Janeiro: IBGE; 2022 [accesado en 04/04/2022]. Disponible en: https://www.ibge.gov.br/cidades-e-estados/rj/petropolis.html.

Aguiar M, Van-Dierdonck JB, Mar J, Cusimano N, Knopoff D, Anam V, Stollenwerk N. Critical fluctuations in epidemic models explain

COVID-19 post-lockdown dynamics. Sci Rep. 2021 Jul 5;11(1):13839. doi: 10.1038/s41598-021-93366-7.

Randolph HE, Barreiro LB. Herd Immunity: Understanding COVID-19. Immunity. 2020 May 19;52(5):737-741. doi: 10.1016/j.immuni.

04.012.

Oliveira Júnior GA de. Redefinição da centralidade urbana em cidades médias. Soc Nat. 2008 Jun;20(1):205-20. doi: 10.1590/S1982-