Local Moran Index: an application in epidemiological coefficients of the COVID-19 pandemic in Brazil

Authors

DOI:

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

Keywords:

Brazil; COVID-19; Epidemiological coefficients; Local Moran Index.

Abstract

The COVID-19 pandemic spread quickly around the world in a frightening way. In Brazil, the third country in the world with the highest number of people infected and killed by the disease, it is important that the government health authorities identify the federation units that stand out in cases and deaths due to this disease for targeting resources. The Local Moran Index is a statistical tool that estimates those units of the federation that stands out the most with some statistical significance. We used the epidemiological coefficients of incidence, prevalence, and lethality to describe Brazil’s pandemic better today. We use R software to obtain maps and results.

References

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

Downloads

Published

16/03/2021

How to Cite

ALVES, H. J. de P. .; FERNANDES, F. A.; LIMA, K. P. de .; BATISTA, B. D. de O.; FERNANDES, T. J. Local Moran Index: an application in epidemiological coefficients of the COVID-19 pandemic in Brazil . 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: 18 apr. 2021.

Issue

Section

Health Sciences