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• W3148769126 abstract "Exploring spatio-temporal patterns of disease incidence can help to identify areas of significantly elevated or decreased risk, providing potential etiologic clues. The study uses the retrospective analysis of space-time scan statistic to detect the clusters of COVID-19 in mainland China with a different maximum clustering radius at the family-level based on case dates of onset. The results show that the detected clusters vary with the clustering radius. Forty-three space-time clusters were detected with a maximum clustering radius of 100 km and 88 clusters with a maximum clustering radius of 10 km from 2 December 2019 to 20 June 2020. Using a smaller clustering radius may identify finer clusters. Hubei has the most clusters regardless of scale. In addition, most of the clusters were generated in February. That indicates China’s COVID-19 epidemic prevention and control strategy is effective, and they have successfully prevented the virus from spreading from Hubei to other provinces over time. Well-developed provinces or cities, which have larger populations and developed transportation networks, are more likely to generate space-time clusters. The analysis based on the data of cases from onset may detect the start times of clusters seven days earlier than similar research based on diagnosis dates. Our analysis of space-time clustering based on the data of cases on the family-level can be reproduced in other countries that are still seriously affected by the epidemic such as the USA, India, and Brazil, thus providing them with more precise signals of clustering." @default.
• W3148769126 created "2021-04-13" @default.
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• W3148769126 date "2021-03-30" @default.
• W3148769126 modified "2023-10-07" @default.
• W3148769126 title "Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis" @default.
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• W3148769126 doi "https://doi.org/10.3390/ijerph18073583" @default.
• W3148769126 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8037204" @default.
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