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Article

Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

by 1,2, 3,*, 1,2 and 3,4
1
National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China
2
Hunan Provincial Key Laboratory of Geo-information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411100, China
3
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
4
Shenzhen Key Laboratory of Spatial Smart Sensing and Service, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(7), 2563; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17072563
Received: 28 February 2020 / Revised: 31 March 2020 / Accepted: 1 April 2020 / Published: 8 April 2020
(This article belongs to the Collection Outbreak of a Novel Coronavirus: A Global Health Threat)
Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic. View Full-Text
Keywords: 2019 novel coronavirus; geographic information science; abrupt change; spatial cluster; spatial outlier; daily new confirmed cases; incidence rates 2019 novel coronavirus; geographic information science; abrupt change; spatial cluster; spatial outlier; daily new confirmed cases; incidence rates
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MDPI and ACS Style

Yang, W.; Deng, M.; Li, C.; Huang, J. Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China. Int. J. Environ. Res. Public Health 2020, 17, 2563. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17072563

AMA Style

Yang W, Deng M, Li C, Huang J. Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China. International Journal of Environmental Research and Public Health. 2020; 17(7):2563. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17072563

Chicago/Turabian Style

Yang, Wentao, Min Deng, Chaokui Li, and Jincai Huang. 2020. "Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China" International Journal of Environmental Research and Public Health 17, no. 7: 2563. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17072563

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