Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries
Abstract Background Coronavirus disease 2019 (COVID-19) has become a pandemic infectious disease and become a serious public health crisis. As the COVID-19 pandemic continues to spread, it is of vital importance to detect COVID-19 clusters to better distribute resources and optimizing measures. This...
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ftdoajarticles:oai:doaj.org/article:d11e6033c6e14737a33f4e6222926161 2023-05-15T16:11:04+02:00 Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries Mingjin Xue Zhaowei Huang Yudi Hu Jinlin Du Miao Gao Ronglin Pan Yuqian Mo Jinlin Zhong Zhigang Huang 2022-11-01T00:00:00Z https://doi.org/10.1186/s12889-022-14298-z https://doaj.org/article/d11e6033c6e14737a33f4e6222926161 EN eng BMC https://doi.org/10.1186/s12889-022-14298-z https://doaj.org/toc/1471-2458 doi:10.1186/s12889-022-14298-z 1471-2458 https://doaj.org/article/d11e6033c6e14737a33f4e6222926161 BMC Public Health, Vol 22, Iss 1, Pp 1-15 (2022) COVID-19 Space–time clusters Predict STSS Monitor Public aspects of medicine RA1-1270 article 2022 ftdoajarticles https://doi.org/10.1186/s12889-022-14298-z 2022-12-30T20:09:08Z Abstract Background Coronavirus disease 2019 (COVID-19) has become a pandemic infectious disease and become a serious public health crisis. As the COVID-19 pandemic continues to spread, it is of vital importance to detect COVID-19 clusters to better distribute resources and optimizing measures. This study helps the surveillance of the COVID-19 pandemic and discovers major space–time clusters of reported cases in European countries. Prospective space–time scan statistics are particularly valuable because it has detected active and emerging COVID-19 clusters. It can prompt public health decision makers when and where to improve targeted interventions, testing locations, and necessary isolation measures, and the allocation of medical resources to reduce further spread. Methods Using the daily case data of various countries provided by the European Centers for Disease Control and Prevention, we used SaTScan™ 9.6 to conduct a prospective space–time scan statistics analysis. We detected statistically significant space–time clusters of COVID-19 at the European country level between March 1st to October 2nd, 2020 and March 1st to October 2nd, 2021. Using ArcGIS to draw the spatial distribution map of COVID-19 in Europe, showing the emerging clusters that appeared at the end of our study period detected by Poisson prospective space–time scan statistics. Results The results show that among the 49 countries studied, the regions with the largest number of reported cases of COVID-19 are Western Europe, Central Europe, and Eastern Europe. Among the 49 countries studied, the country with the largest cumulative number of reported cases is the United Kingdom, followed by Russia, Turkey, France, and Spain. The country (or region) with the lowest cumulative number of reported cases is the Faroe Islands. We discovered 9 emerging clusters, including 21 risky countries. Conclusion This result can provide timely information to national public health decision makers. For example, a country needs to improve the allocation of medical ... Article in Journal/Newspaper Faroe Islands Directory of Open Access Journals: DOAJ Articles Faroe Islands BMC Public Health 22 1 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
COVID-19 Space–time clusters Predict STSS Monitor Public aspects of medicine RA1-1270 |
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COVID-19 Space–time clusters Predict STSS Monitor Public aspects of medicine RA1-1270 Mingjin Xue Zhaowei Huang Yudi Hu Jinlin Du Miao Gao Ronglin Pan Yuqian Mo Jinlin Zhong Zhigang Huang Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries |
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COVID-19 Space–time clusters Predict STSS Monitor Public aspects of medicine RA1-1270 |
description |
Abstract Background Coronavirus disease 2019 (COVID-19) has become a pandemic infectious disease and become a serious public health crisis. As the COVID-19 pandemic continues to spread, it is of vital importance to detect COVID-19 clusters to better distribute resources and optimizing measures. This study helps the surveillance of the COVID-19 pandemic and discovers major space–time clusters of reported cases in European countries. Prospective space–time scan statistics are particularly valuable because it has detected active and emerging COVID-19 clusters. It can prompt public health decision makers when and where to improve targeted interventions, testing locations, and necessary isolation measures, and the allocation of medical resources to reduce further spread. Methods Using the daily case data of various countries provided by the European Centers for Disease Control and Prevention, we used SaTScan™ 9.6 to conduct a prospective space–time scan statistics analysis. We detected statistically significant space–time clusters of COVID-19 at the European country level between March 1st to October 2nd, 2020 and March 1st to October 2nd, 2021. Using ArcGIS to draw the spatial distribution map of COVID-19 in Europe, showing the emerging clusters that appeared at the end of our study period detected by Poisson prospective space–time scan statistics. Results The results show that among the 49 countries studied, the regions with the largest number of reported cases of COVID-19 are Western Europe, Central Europe, and Eastern Europe. Among the 49 countries studied, the country with the largest cumulative number of reported cases is the United Kingdom, followed by Russia, Turkey, France, and Spain. The country (or region) with the lowest cumulative number of reported cases is the Faroe Islands. We discovered 9 emerging clusters, including 21 risky countries. Conclusion This result can provide timely information to national public health decision makers. For example, a country needs to improve the allocation of medical ... |
format |
Article in Journal/Newspaper |
author |
Mingjin Xue Zhaowei Huang Yudi Hu Jinlin Du Miao Gao Ronglin Pan Yuqian Mo Jinlin Zhong Zhigang Huang |
author_facet |
Mingjin Xue Zhaowei Huang Yudi Hu Jinlin Du Miao Gao Ronglin Pan Yuqian Mo Jinlin Zhong Zhigang Huang |
author_sort |
Mingjin Xue |
title |
Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries |
title_short |
Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries |
title_full |
Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries |
title_fullStr |
Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries |
title_full_unstemmed |
Monitoring European data with prospective space–time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries |
title_sort |
monitoring european data with prospective space–time scan statistics: predicting and evaluating emerging clusters of covid-19 in european countries |
publisher |
BMC |
publishDate |
2022 |
url |
https://doi.org/10.1186/s12889-022-14298-z https://doaj.org/article/d11e6033c6e14737a33f4e6222926161 |
geographic |
Faroe Islands |
geographic_facet |
Faroe Islands |
genre |
Faroe Islands |
genre_facet |
Faroe Islands |
op_source |
BMC Public Health, Vol 22, Iss 1, Pp 1-15 (2022) |
op_relation |
https://doi.org/10.1186/s12889-022-14298-z https://doaj.org/toc/1471-2458 doi:10.1186/s12889-022-14298-z 1471-2458 https://doaj.org/article/d11e6033c6e14737a33f4e6222926161 |
op_doi |
https://doi.org/10.1186/s12889-022-14298-z |
container_title |
BMC Public Health |
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22 |
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1 |
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1765996185339822080 |