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Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil
BACKGROUND: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Sociedade Brasileira de Medicina Tropical - SBMT
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9344939/ https://www.ncbi.nlm.nih.gov/pubmed/35946634 http://dx.doi.org/10.1590/0037-8682-0607-2021 |
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author | Ferreira, Ricardo Vicente Martines, Marcos Roberto Toppa, Rogério Hartung de Assunção, Luiza Maria Desjardins, Michael Richard Delmelle, Eric |
author_facet | Ferreira, Ricardo Vicente Martines, Marcos Roberto Toppa, Rogério Hartung de Assunção, Luiza Maria Desjardins, Michael Richard Delmelle, Eric |
author_sort | Ferreira, Ricardo Vicente |
collection | PubMed |
description | BACKGROUND: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. METHODS: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. RESULTS: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. CONCLUSIONS: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions. |
format | Online Article Text |
id | pubmed-9344939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Sociedade Brasileira de Medicina Tropical - SBMT |
record_format | MEDLINE/PubMed |
spelling | pubmed-93449392022-08-18 Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil Ferreira, Ricardo Vicente Martines, Marcos Roberto Toppa, Rogério Hartung de Assunção, Luiza Maria Desjardins, Michael Richard Delmelle, Eric Rev Soc Bras Med Trop Major Article BACKGROUND: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. METHODS: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. RESULTS: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. CONCLUSIONS: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions. Sociedade Brasileira de Medicina Tropical - SBMT 2022-08-05 /pmc/articles/PMC9344939/ /pubmed/35946634 http://dx.doi.org/10.1590/0037-8682-0607-2021 Text en https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License |
spellingShingle | Major Article Ferreira, Ricardo Vicente Martines, Marcos Roberto Toppa, Rogério Hartung de Assunção, Luiza Maria Desjardins, Michael Richard Delmelle, Eric Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil |
title | Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil |
title_full | Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil |
title_fullStr | Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil |
title_full_unstemmed | Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil |
title_short | Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil |
title_sort | utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the state of são paulo, brazil |
topic | Major Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9344939/ https://www.ncbi.nlm.nih.gov/pubmed/35946634 http://dx.doi.org/10.1590/0037-8682-0607-2021 |
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