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

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  • Wentao Yang

    (National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China
    Hunan Provincial Key Laboratory of Geo-information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411100, China)

  • Min Deng

    (School of Geosciences and Info-physics, Central South University, Changsha 410083, China)

  • Chaokui Li

    (National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China
    Hunan Provincial Key Laboratory of Geo-information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411100, China)

  • Jincai Huang

    (School of Geosciences and Info-physics, Central South University, Changsha 410083, China
    Shenzhen Key Laboratory of Spatial Smart Sensing and Service, Shenzhen University, Shenzhen 518060, China)

Abstract

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.

Suggested Citation

  • Wentao Yang & Min Deng & Chaokui Li & Jincai Huang, 2020. "Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China," IJERPH, MDPI, vol. 17(7), pages 1-11, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2563-:d:343025
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    References listed on IDEAS

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    1. A. N. Pettitt, 1979. "A Non‐Parametric Approach to the Change‐Point Problem," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(2), pages 126-135, June.
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    Cited by:

    1. Melissa Silva & Iuria Betco & César Capinha & Rita Roquette & Cláudia M. Viana & Jorge Rocha, 2022. "Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    2. Xin Li & Peixin Lu & Lianting Hu & Tianhui Huang & Long Lu, 2020. "Factors Associated with Mental Health Results among Workers with Income Losses Exposed to COVID-19 in China," IJERPH, MDPI, vol. 17(15), pages 1-11, August.
    3. Mohd Sahrul Syukri Yahya & Edie Ezwan Mohd Safian & Burhaida Burhan, 2020. "The Real-Time Situation of Covid-19 Pandemic between MCO, CMCO and RMCO Using Geographic Information System (GIS): Study Case in Malaysia," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 4(10), pages 75-81, October.

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