Índice de Moran Local: uma aplicação em coeficientes epidemiológicos da pandemia de COVID-19 no Brasil

Autores

DOI:

https://doi.org/10.33448/rsd-v10i3.13472

Palavras-chave:

Brasil; COVID-19; Coeficientes epidemiológicos,; Índice de Moran local.

Resumo

A pandemia da COVID-19 se espalhou rapidamente pelo mundo de uma forma assustadora. No Brasil, terceiro país do mundo com maior número de infectados e mortos pela doença, é importante que as autoridades sanitárias governamentais identifiquem as unidades da federação que se destacam nos casos e óbitos por essa doença para o direcionamento dos recursos. O Índice de Moran Local é uma ferramenta estatística que estima as unidades da federação que mais se destacam com alguma significância estatística. Usamos os coeficientes epidemiológicos de incidência, prevalência e letalidade para descrever melhor a pandemia no Brasil hoje. Usamos o software R para obter os mapas e resultados.

Referências

Alves, H. J. de P., Fernandes, F. A., Lima, K. P. de, Batista, B. D. de O. & Fernandes, T. J. (2020). A pandemia COVID-19 no Brasil: uma aplicação dos k-means método de agrupamento. Research, Society and Development, 9(10), e5829109059, 1-21.10.33448 / rsd-v9i10.9059.

Amaral, M., Conceição, K., Andrade, M., & Padovani, C. (2020). Generalized growth curve model for covid-19 in brazilian states. Revista Brasileira de Biometria, 38(2), 125-146. 10.28951/rbb.v38i2.481

Arashi, M., Bekker, A., Salehi, M., Millard, S., Erasmus, B., Cronje, T., & Golpaygani, M., (2020). Spatial analysis and prediction of covid-19 spread in south africa after lockdown. arXiv preprint arXiv:2005.09596.

Birch, C., Chikukwa, A., Hyder, K. & Vilas, V. (2009). Spatial distribution of the active surveillance of sheep scrapie in great britain: An exploratory analysis. BMC veterinary research, (5)23, 1-14. 10.1186/1746-6148-5-23.

Cordes, J. & Castro, M. C. (2020). Spatial analysis of covid-19 clusters and contextual factors in New York City. Spatial and Spatio-temporal Epidemiology 34, 1-8. https://doi.org/10.1016/j.sste.2020.100355

Fernandes, F. A., Alves, H. J. de P., Fernandes, T. J., & Muniz, J. A. (2020). Overview of the initial growth phase in the number of cases and deaths caused by COVID-19 in Brazil. Research, Society and Development, 9(10), e1539108560. https://doi.org/10.33448/rsd-v9i10.8560

Gehlen, M., Nicola, M. R., Costa, E. R., Cabral, V. K., de Quadros, E. L., Chaves, C. O., Lahm, R. A., Nicolella, A. D., Rossetti, M. L. & Silva, D. R. (2019). Geospatial intelligence and health analitycs: Its application and utility in a city with high tuberculosis incidence in brazil. Journal of infection and public health 12(5), 681–689. https://doi.org/10.1016/j.jiph.2019.03.012

Griffith, D. A., Wong, D. W. & Whitfield, T. (2003). Exploring relationships between the global and regional measures of spatial autocorrelation. Journal of Regional Science 43, 683–710. https://doi.org/10.1111/j.0022-4146.2003.00316.x

Hendricks, B. & Mark-Carew, M., (2017). Using exploratory data analysis to identify and predict patterns of human lyme disease case clustering within a multistate region, 2010–2014. Spatial and spatio-temporal epidemiology 20, 35–43. https://doi.org/10.1016/j.sste.2016.12.003

Huang, R., Liu, M., & Ding, Y. (2020). Spatial-temporal distribution of covid-19 in China and its prediction: A data-driven modeling analysis. The Journal of Infection in Developing Countries 14(3), 246–253. https://doi.org/10.3855/jidc.12585

Kang, D., Choi, H., Kim, J. H. & Choi, J. (2020). Spatial epidemic dynamics of the covid-19 outbreak in China. International Journal of Infectious Diseases, 94, 96-102. https://doi.org/10.1016/j.ijid.2020.03.076

Khailany, R. A., Safdar, M. & Ozaslan, M. (2020). Genomic characterization of a novel sars-cov-2. Gene reports, 19, 100682. https://doi.org/10.1016/j.genrep.2020.100682

Kim, S. & Castro, M. C. (2020). Spatiotemporal pattern of covid-19 and government response in south korea (as of may 31, 2020). International Journal of Infectious Diseases 98, 328–333. https://doi.org/10.1016/j.ijid.2020.07.004

Koh, K., Grady, S. C., Darden, J. T. & Vojnovic, I. (2018). Adult obesity prevalence at the county level in the united states, 2000–2010: downscaling public health survey data using a spatial microsimulation approach. Spatial and spatio-temporal epidemiology 26, 153–164. https://doi.org/10.1016/j.sste.2017.10.001

Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics-Theory and methods, 26, 1481–1496. https://doi.org/10.1080/03610929708831995

Letko, M., Marzi, A. & Munster, V. (2020). Functional assessment of cell entry and receptor usage for sars-cov-2 and other lineage b betacoronaviruses. Nature microbiology, 5, 562-9. https://doi.org/10.1038/s41564-020-0688-y

Lew, D. & Rigdon, S. E. (2019). Mapping rates of inpatient hospitalizations related to mental disorders in the state of missouri: a conditional autoregressive model with zip code-level data. Spatial and spatio-temporal epidemiology 28, 24–32. https://doi.org/10.1016/j.sste.2018.11.003

Li, H., Li, H., Ding, Z., Hu, Z., Chen, F., Wang, K., Peng, Z. & Shen, H. (2020). Spatial statistical analysis of coronavirus disease 2019 (covid-19) in China. Geospatial Health 15(1). 11-18. https://doi.org/10.4081/gh.2020.867

Lieberman-Cribbin, W., Tuminello, S., Flores, R. M. & Taioli, E. (2020). Disparities in covid-19 testing and positivity in new york city. American journal of preventive medicine 59, 326–332. https://doi.org/10.1016/j.amepre.2020.06.005

Monteiro, A. M. V., Câmara, G., Carvalho, M. & Druck, S. (2004). Análise espacial de dados geográficos. Embrapa.

Nassiri, R. (2020). Perspective on wuhan viral pneumonia. Advances in Public Health, Community and tropical Medicine, (2), 1-3.

Nilima, N., Kaushik, S., Tiwary, B. & Pandey, P. K. (2021). Psycho-social factors associated with the nationwide lockdown in india during covid-19 pandemic. Clinical Epidemiology and Global Health, 9, 47-52. https://doi.org/10.1016/j.cegh.2020.06.010

Pereira,A.S., Shitsuka, D. M., Parreira, F. J., & Shitsuka R. (2018). Metodologia da pesquisa científica. UFSM. https://repositorio.ufsm.br/bitstream/hand le/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1.

Pinto, E., Santos, G. & Oliveira, F. (2014). Análise espaço-temporal aplicada às ocorrências de hipertensão e diabetes nos municípios do estado de minas gerais. Revista Brasileira de Biometria 32(2), 238–266.

R Core Team, (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. URL: https://www.R-project.org/.

Salathé, M., Althaus, C. L., Neher, R., Stringhini, S., Hodcroft, E., Fellay, J., Zwahlen, M., Senti, G., Battegay, M. & Wilder-Smith, A. (2020). Covid-19 epidemic in switzerland: on the importance of testing, contact tracing and isolation. Swiss medical weekly 150, w20225, 1-3. https://doi.org/10.4414/smw.2020.20225

Velavan, T. & Meyer, C. (2020). La epidemia de covid-19. Tropical Medicine and International Health, 25(3), 278-280. 10.1111/tmi.13383

Wang, C., Horby, P. W., Hayden, F. G. & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The Lancet, 395, 470–473. https://doi.org/10.1016/S0140-6736(20)30185-9

Werneck, G. L., & Carvalho, M. S. (2020). A pandemia de covid-19 no brasil: crônica de uma crise sanitária anunciada. Cadernos de Saúde Pública 36(5):e00068820, 1-4. https://doi.org/10.1590/0102-311X00068820

Yao, Y., Pan, J., Wang, W., Liu, Z., Kan, H., Qiu, Y., Meng, X. & Wang, W. (2020). Association of particulate matter pollution and case fatality rate of covid-19 in 49 chinese cities. Science of the Total Environment 741, 140396. https://doi.org/10.1016/j.scitotenv.2020.140396

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Publicado

16/03/2021

Como Citar

ALVES, H. J. de P. .; FERNANDES, F. A.; LIMA, K. P. de .; BATISTA, B. D. de O.; FERNANDES, T. J. Índice de Moran Local: uma aplicação em coeficientes epidemiológicos da pandemia de COVID-19 no Brasil. Research, Society and Development, [S. l.], v. 10, n. 3, p. e27810313472, 2021. DOI: 10.33448/rsd-v10i3.13472. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/13472. Acesso em: 4 jul. 2024.

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Ciências da Saúde