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Article

Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis

by
Min Xu
1,2,
Chunxiang Cao
1,
Xin Zhang
1,
Hui Lin
3,
Zhong Yao
4,*,
Shaobo Zhong
5,
Zhibin Huang
1 and
Robert Shea Duerler
1
1
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
3
China Electronic Technology Group Corporation, Institute of Electronic Science, Beijing 100041, China
4
Jiangxi Academy of Sciences, Nanchang 330098, China
5
Beijing Research Center of Urban Systems Engineering, Xizhimen Nan Da Jie 16, Xicheng District, Beijing 100035, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(7), 3583; https://doi.org/10.3390/ijerph18073583
Submission received: 23 February 2021 / Revised: 22 March 2021 / Accepted: 23 March 2021 / Published: 30 March 2021
(This article belongs to the Special Issue A Public Health View on COVID-19: Epidemiology, Implications and Responses)
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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.
Keywords: COVID-19; GIS; space-time cluster; retrospective analysis; fine-scale COVID-19; GIS; space-time cluster; retrospective analysis; fine-scale

Share and Cite

MDPI and ACS Style

Xu, M.; Cao, C.; Zhang, X.; Lin, H.; Yao, Z.; Zhong, S.; Huang, Z.; Shea Duerler, R. Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis. Int. J. Environ. Res. Public Health 2021, 18, 3583. https://doi.org/10.3390/ijerph18073583

AMA Style

Xu M, Cao C, Zhang X, Lin H, Yao Z, Zhong S, Huang Z, Shea Duerler R. Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis. International Journal of Environmental Research and Public Health. 2021; 18(7):3583. https://doi.org/10.3390/ijerph18073583

Chicago/Turabian Style

Xu, Min, Chunxiang Cao, Xin Zhang, Hui Lin, Zhong Yao, Shaobo Zhong, Zhibin Huang, and Robert Shea Duerler. 2021. "Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis" International Journal of Environmental Research and Public Health 18, no. 7: 3583. https://doi.org/10.3390/ijerph18073583

APA Style

Xu, M., Cao, C., Zhang, X., Lin, H., Yao, Z., Zhong, S., Huang, Z., & Shea Duerler, R. (2021). Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis. International Journal of Environmental Research and Public Health, 18(7), 3583. https://doi.org/10.3390/ijerph18073583

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