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.
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http://dx.doi.org/10.1590/0037-8682-0607-2021 | DOI Listing |
Ann Med
December 2025
Infectious disease Control Department, Quzhou Center for Disease Control and Prevention, Quzhou, Zhejiang Province, China.
Background: The global seasonal influenza activity has decreased during the coronavirus disease 2019 (COVID-19) pandemic. Non-pharmaceutical interventions (NPIs), such as reducing gatherings and wearing masks, can have varying impacts on the spread of influenza. We aim to analyse the basic characteristics, epidemiology and space-time clustering of influenza in Quzhou city before and after the COVID-19 pandemic based on five years of surveillance data.
View Article and Find Full Text PDFZhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
August 2024
School of Public Health, Hainan Medical University, Haikou, Hainan 571199, China.
Objective: To investigate the spatiotemporal distribution characteristics and potential influencing factors of newly diagnosed echinococcosis cases in Qinghai Province from 2016 to 2022, so as to provide insights into the formulation of the echinococcosis control strategy in Qinghai Province.
Methods: The number of individuals screened for echinococcosis, number of newly diagnosed echinococcosis cases, number of registered dogs and number of stray dogs were captured from the annual reports of echinococcosis control program in Qinghai Province from 2016 to 2022, and the detection of newly diagnosed echinococcosis cases was calculated. The number of populations, precipitation, temperature, wind speed, sunshine hours, average altitude, number of year-end cattle stock, number of year-end sheep stock, gross domestic product (GDP) per capita, and number of village health centers in each county (district) of Qinghai Province were captured from the , and county-level electronic maps in Qinghai Province were downloaded from the National Platform for Common Geospatial Information Services.
Sci Rep
December 2024
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Cad Saude Publica
November 2024
Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil.
Respiratory diseases pose a significant threat to the health of the Brazilian population, ranking among the leading causes of hospitalizations and deaths in the country. The most impacted demographics are children, adolescents, and older adults, who respectively have the highest rates of hospitalizations and deaths. An exploratory ecological study was conducted to assess the spatio-temporal distribution of hospitalizations and deaths due to respiratory diseases among children, adolescents, and older adults residing in municipalities in the Brazilian Legal Amazon.
View Article and Find Full Text PDFBMC Public Health
October 2024
Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu, 211166, China.
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