Análise espacial e espaço-temporal da Covid-19 no estado da Paraíba

Catarina Ferreira Pontes; Jodavid de Araújo Ferreira; Claryce Rebeca de Sousa Feitosa; Rodrigo Pinheiro de Toledo Vianna; Ronei Marcos  de Moraes

doi:10.26512/2236-56562023e41886

Authors

Catarina Ferreira Pontes Universidade Federal da Paraíba https://orcid.org/0000-0003-4017-3320
Jodavid de Araújo Ferreira Universidade Federal da Paraíba https://orcid.org/0000-0002-2131-6464
Claryce Rebeca de Sousa Feitosa Universidade Federal da Paraíba https://orcid.org/0000-0001-5782-130X
Rodrigo Pinheiro de Toledo Vianna Universidade Federal da Paraíba https://orcid.org/0000-0002-5358-1967
Ronei Marcos de Moraes Universidade Federal da Paraíba https://orcid.org/0000-0001-8436-8950

DOI:

https://doi.org/10.26512/2236-56562023e41886

Keywords:

Keywords: Coronavirus infections, Spatial analysis, Spatio-temporal analysis.

Abstract

Abstract: This study aims to detect the spatial and spatio-temporal distribution of new weekly COVID-19 cases in the state of Paraíba. Methodology: Epidemiological, retrospective study with a quantitative approach, whose data used for analysis refer to the number of weekly confirmed new cases of COVID-19 reported in the state of Paraíba, Brazil, and which correspond to the period of the 12th epidemiological week from 2020 to the 32nd week epidemiological data for 2021. Spatial Incidence Ratios were estimated and the Circular Scan and Spatio-Time Scan statistics were applied to detect clusters. Results: The spatial analysis was divided into four moments, the first was evidenced by the 12th epidemiological week of 2020, the cases of COVID-19 were present in four municipalities: João Pessoa, Pitimbu, Igaracy and Sousa. The second was comprised by the first peak of the COVID-19 pandemic, in the 28th epidemiological week of 2020, marked by the presence of spatial clusters in all regions of the state, especially in the Northeast region. The third was determined by the 35th epidemiological week of 2020, presenting a spread of cases to the interior of the state of Paraíba. The fourth was characterized by the 22nd epidemiological week of 2021, identifying scattered clusters in all regions of the state of Paraíba. In the space-time analysis, five clusters were observed, most present in the north, south and central regions of the state. Conclusion: The detection of spatial and spatio-temporal clusters can help public managers to recognize priority areas for monitoring COVID-19 cases.

Keywords: Coronavirus infections, Spatial analysis, Spatio-temporal analysis.

References

REFERÊNCIAS BIBLIOGRÁFICAS

ARROYO LH, et al. Identificação de áreas de risco para a transmissão da tuberculose no município de São Carlos, São Paulo, 2008 a 2013.Epidemiol Serv Saúde. 2017;26(3):525-34. DOI: http://dx.doi.org/10.5123/s1679-49742017000300010

BRASIL. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Análise em Saúde e Doenças não Transmissíveis. Guia de vigilância epidemiológica Emergência de saúde pública de Importância nacional pela Doença pelo coronavírus 2019 – covid-19/ Ministério da Saúde, Secretaria de Vigilância em Saúde. – Brasília: Ministério da Saúde, 2021. Disponível em: https://www.conasems.org.br/wp-content/uploads/2021/03/Guia-de-vigila%CC%82ncia-epidemiolo%CC%81gica-da-covid_19_15.03_2021.pdf.

______. Ministério da Saúde. Diretrizes para diagnóstico e tratamento da COVID-

Brasília, DF, 2020. Disponível em: https://portalarquivos.saude.gov.br/images/pdf/2020/May/08/Diretriz-Covid19-v4-07-

20h05m.pdf. Acesso em: 20 nov. 2021.

DENG T, et al. Spatial-temporal clusters and risk factors of hand, foot, and mouth disease at the district level in Guangdong Province, China. PloS one. 2013;8(2):e56943. DOI: https://doi.org/10.1371/journal.pone.0056943

FLOSS, M et al. A pandemia de COVID-19 em territórios rurais e remotos: perspectiva

de médicas e médicos de família e comunidade sobre a atenção primária à saúde.

Cadernos de Saúde Pública [online]. 2020, v. 36, n. 7. ISSN 1678-4464. Acesso em: 20 Janeiro 2022. Disponível em: https://doi.org/10.1590/0102-311X00108920

GOODCHILD, M. Geographical Data Modeling. Computers & Geosciences, v.8, n. 4,

p. 401-408, 1992. https://doi.org/10.1016/0098-3004(92)90069-4

HOLMES, D. C. S. C.; MORAES, R. M.; VIANNA, R. P. T. A Rule for Combination

of Spatial Clustering Methods. In: The Seventh International Conferences on Pervasive

Patterns and Applications, 2015, Nice, França. Anais. Proceedings of the Seventh International Conferences on Pervasive Patterns and Applications. 2015, p. 55-59. DOI: 10.1142/9789813273238_0091.

INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATISTICA - IBGE (2010) População residente, total, urbana total e urbana na sede municipal, em números absolutos e relativos, com indicação da área total e densidade demográfica, segundo as Unidades da Federação e os municípios – 2010. Brasília: IBGE. Disponível em: https://www.ibge.gov.br/

KULLDORFF, M.; NAGARWALLA, N. Spatial disease clusters: detection and inference. Statistics in Medicine, v.14, p. 799-810, 1995. Disponível em: https://pubmed.ncbi.nlm.nih.gov/7644860/

KULLDORFF, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6):1481–1496. Disponível em: https://www.tandfonline.com/doi/abs/10.1080/03610929708831995

LI, R., et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science, 2020. 368(6490), 489-493. Disponível em: https://pubmed.ncbi.nlm.nih.gov/32179701/

LIMA, LMM, et al. A new combination rule for Spatial Decision Support Systems for

epidemiology. Int J Health Geogr. 2019; 18:25. Disponível em: https://ijhealthgeographics.biomedcentral.com/articles/10.1186/s12

MORAES, R. M.; NOGUEIRA, J. A.; SOUSA, A. C. A. (2014) A New Architecture for a Spatio-Temporal Decision Support System for Epidemiological Purposes. In: 11th International FLINS Conference on Decision Making and Soft Computing. Anais... João Pessoa (Brasil): FLINS 2014, p. 17-23. Disponível em: https://www.worldscientific.com/doi/abs/10.1142/9789814619998_0006

ORGANIZAÇÃO MUNDIAL DE SAÚDE. WHO Coronavirus Disease (COVID19) Dashboard. Publicado em 2021. Acesso em 04 maio 2021. Disponível em: https://covid19.who.int/

OUSLANDER, JG. Coronavirus Disease19 in Geriatrics and Long-Term Care: An Update. J Am Geriatr Soc. 2020 May;68(5):918-921. doi: 10.1111/jgs.16464. Epub 2020 Apr 16. PMID: 32243567. Disponível em: https://pubmed.ncbi.nlm.nih.gov/32243567/

PARAÍBA (Estado). Secretaria de Saúde do Estado da Paraíba. Paraíba, PB, 2020c.

Disponível em: https://paraiba.pb.gov.br/diretas/saude. Acesso em: 25 dez 2021.

PEDROSA, N. L.; ALBUQUERQUE, N. L. S. Análise Espacial dos Casos de COVID-

e leitos de terapia intensiva no estado do Ceará, Brasil. Ciênc. saúde coletiva, Rio

de Janeiro, v. 25, p. 2461-8, 2020. Suppl. 1. https://doi.org/10.1590/1413-

1.10952020.

ROCHE LM, et al. Disparities in Female Breast Cancer Stage at Diagnosis in New Jersey: A Spatial-Temporal Analysis. J Public Health Manag Pract. 2017; 23(5):477-86. DOI: https://doi.org/10.1097/PHH.0000000000000524

SÁ, L.R, et al, "New Combining Rules for Spatial Clustering Methods Using Sigma-Count for Spatial Epidemiology," 2020 International Conference on Decision Aid Sciences and Application (DASA), 2020, pp. 794-800, DOI: 10.1109/DASA51403.2020.9317161. Disponível em: https://ieeexplore.ieee.org/document/9317161

SOUZA ALP. Geoprocessamento e Vigilância Epidemiológica no enfrentamento da

Covid-19 na Macrorregião Triangulo Sul, MG. Ra. e Rum. 4º de dezembro de 2020

[acesso 13º de maio de 2021];8(1):189-201. Disponível em:

http://www.seer.unirio.br/raizeserumos/article/view/10389

TANGO, T. Statistical Methods for Disease Clustering. Springer, 2010. Disponível em: https://link.springer.com/book/10.1007/978-1-4419-1572-6

YAMADA, I.; THILL, J-C. (2007) Local Indicators of Network-Constrained Clusters in Spatial Point Patterns. Geographical Analysis, v. 39, n.3, p. 268–292. Disponível em: https://www.tandfonline.com/doi/abs/10.1080/00045600903550337

ZHAO, W, YU, S, ZHA, X, et al. Clinical characteristics and durations of hospitalized patients with COVID-19 in Beijing: a retrospective cohort study. MedRxiv. 2020. Disponível em: https://www.medrxiv.org/content/10.1101/2020.03.13.20035436v2

SPATIAL AND SPATIO-TEMPORAL ANALYSIS DA COVID-19 IN THE STATE OF PARAÍBA

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