Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil

Submitted: 29 July 2020
Accepted: 17 September 2020
Published: 26 November 2020
Abstract Views: 3402
PDF: 1073
HTML: 16
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

As of 16 May 2020, the number of confirmed cases and deaths in Brazil due to COVID-19 hit 233,142 and 15,633, respectively, making the country one of the most affected by the pandemic. The State of São Paulo (SSP) hosts the largest number of confirmed cases in Brazil, with over 60,000 cases to date. Here we investigate the spatial distribution and spreading patterns of COVID-19 in the SSP by mapping the spatial autocorrelation and the clustering patterns of the virus in relation to the population density and the number of hospital beds. Clustering analysis indicated that São Paulo City is a significant hotspot for both the confirmed cases and deaths, whereas other cities across the state were less affected. Bivariate Moran's I showed a low relationship between the number of deaths and population density, whereas the number of hospital beds was less related, implying that the fatality depends substantially on the actual patients' conditions. Multivariate Local Geary showed a positive relationship between the number of deaths and population density, with two cities near São Paulo City being negatively related; the relationship between the number of deaths and hospital beds availability in the São Paulo Metropolitan Area was basically positive. Social isolation measures throughout the State of São Paulo have been gradually increasing since early March, an action that helped to slow down the emergence of the new confirmed cases, highlighting the importance of the safe-distancing measures in mitigating the local transmission within and between cities in the state.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Acter T, Uddin N, Das J, Akhter A, Choudhury TR, Kim S. Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency, 2020. Sci Total Environ 730: 138996. doi: 10.1016/j.scitotenv.2020.138996. DOI: https://doi.org/10.1016/j.scitotenv.2020.138996
Anselin L. Local indicators of spatial association–LISA, 1995. Geog Anal 27: 93–115. doi: 10.1111/j.1538-4632.1995.tb00338.x. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin L, Sayabri I, Kho Y. GeoDa: An introduction to Spatial Data Analysis, 2006. Geogr Anal 38:5-22. doi: 10.1111/j.0016-7363.2005.00671.x. DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
Anselin L. A local indicator of multivariate spatial association: extending Geary’s c, 2019. Geogr Anal 51:133-150. doi: 10.1111/gean.12164. DOI: https://doi.org/10.1111/gean.12164
Brazil. Ministry of Health – COVID19 – Coronavirus Panel, 2020. https://covid.saude.gov.br/. Accessed 14th May 2020.
Candido DDS et al. Routes for COVID-19 importation in Brazil, 2020, Jour Travel Med 27:taaa042. doi: 10.1093/jtm/taaa042. DOI: https://doi.org/10.1093/jtm/taaa042
Djalante R, Shaw R, DeWit A. Building resilience against biological hazards and pandemics: COVID-19 and its implications for the Sendai Framework, 2020. Prog Disaster Sci 6:100080. doi: 10.1016/j.pdisas.2020.100080. DOI: https://doi.org/10.1016/j.pdisas.2020.100080
IBGE (Brazilian Institute of Geography and Statistics), Population Estimation 2019, 2019. (Brasília).
Kraemer et al. The effect of human mobility and control measures on the COVID-19 epidemic in China, Science 368:493–497. doi: 10.1126/science.abb4218. DOI: https://doi.org/10.1126/science.abb4218
Li H et al. Spatial statistics analysis of Coronavirus Disease 2019 (Covid-19) in China, 2020. Geo Health 15:11-18. doi: 10.4081/gh.2020.867. DOI: https://doi.org/10.4081/gh.2020.867
Lu R. et al. Genomic characterization and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding, 2020. Lancet 395:565-574. doi: 10.1016/ S0140-6736(20)30251-8.
Middelburg RA, Rosendaal FR. COVID-19: How to make between-country comparisons, 2020. Int Jour Infec Dis 96:477-481. doi: 10.1016/j.ijid.2020.05.066. DOI: https://doi.org/10.1016/j.ijid.2020.05.066
Moran PAP. Notes on continuous stochastic phenomena, 1950. Biometrika 37:17–23. doi: 10.2307/2332142. DOI: https://doi.org/10.2307/2332142
Munster VJ, Koopmans M, Van Doremalen N, Van Riel D, Wit E. A novel coronavirus emerging in china - key questions for impact assessment, 2020. N Eng J Med 382:692-4. doi: 10.1056/NEJMp2000929. DOI: https://doi.org/10.1056/NEJMp2000929
Nakada LYK, Urban RC. COVID-19 pandemic: Impacts on the air quality during the partial lockdown in São Paulo state, Brazil, 2020. Sci Total Environ 730:139087. doi: 10.1016/j.scitotenv.2020.139087. DOI: https://doi.org/10.1016/j.scitotenv.2020.139087
Requia WJ, Kondo EK, Adams MD, Gold DR, Struchiner CJ. Risk of the Brazilian health care system over 5572 municipalities to exceed health care capacity due to the 2019 novel coronavirus (COVID-19), 2020. Sci Total Environ 730:139144. doi:10.1016/j.scitotenv.2020.139144. DOI: https://doi.org/10.1016/j.scitotenv.2020.139144
São Paulo. São Paulo State – Social Isolation Intelligent Monitoring System, 2020. https://www.saopaulo.sp.gov.br/coronavirus/isolamento/, Accessed 14 May 2020.
SEADE. Coronavirus, 2020. https://www.seade.gov.br/coronavirus/, Accessed 14th May 2020.
Tommy T et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins, 2020. Nature 583:282-285. doi: 10.1038/s41586-020-2169-0. DOI: https://doi.org/10.1038/s41586-020-2169-0
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z. Clinical characteristics of 138 hospitalized patients with 2019, novel coronavirus–infected pneumonia in Wuhan China, 2020. JAMA 323:1061-1069. doi: 10.1001/jama.2020.1585. DOI: https://doi.org/10.1001/jama.2020.1585
WHO - World Health Organization. Statement on the meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV) [Internet]. Geneva: World Health Organization; 2020 [cited 2020 Mar 4]. Available from: https://www.who.int/news-room/detail/23-01-2020-statement-on-the-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov).
WHO - World Health Organization. Novel coronavirus (2019-nCoV): situation report - 22 [Internet]. Geneva: World Health Organization; 2020 [cited 2020 Mar 4]. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200211-sitrep-22-ncov.pdf?sfvrsn=fb6d49b1_2.
Zhu N et al. A novel coronavirus from patients with pneumonia in China, 2020. N Eng J Med 382:727-733. doi: 10.1056/NEJMoa2001017. DOI: https://doi.org/10.1056/NEJMoa2001017
Zhou P. A pneumonia outbreak associated with a new coronavirus of probable bat origin, 2020. Nature 579:270-273. doi: 10.1038/s41586-020-2012-7. DOI: https://doi.org/10.1038/s41586-020-2012-7

How to Cite

Alcântara, E., Mantovani, J., Rotta, L., Park, E., Rodrigues, T., Campos Carvalho, F., & Roberto Souza Filho, C. (2020). Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.925