IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i7p2563-d343025.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/7/2563/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/7/2563/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stephen J. Déry & Marco A. Hernández-Henríquez & Tricia A. Stadnyk & Tara J. Troy, 2021. "Vanishing weekly hydropeaking cycles in American and Canadian rivers," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Kazi Ali Tamaddun & Ajay Kalra & Sajjad Ahmad, 2019. "Spatiotemporal Variation in the Continental US Streamflow in Association with Large-Scale Climate Signals Across Multiple Spectral Bands," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1947-1968, April.
    3. Jie Yang & Yimin Wang & Jun Yao & Jianxia Chang & Guoxin Xu & Xin Wang & Hui Hu, 2020. "Coincidence probability analysis of hydrologic low-flow under the changing environment in the Wei River Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 1711-1726, September.
    4. Alina Bărbulescu & Cristian Ștefan Dumitriu, 2021. "On the Connection between the GEP Performances and the Time Series Properties," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
    5. Alfredas Račkauskas & Martin Wendler, 2020. "Convergence of U-processes in Hölder spaces with application to robust detection of a changed segment," Statistical Papers, Springer, vol. 61(4), pages 1409-1435, August.
    6. Hsin-Yu Chen & Yu-Hsiang Hsu & Chia-Chi Huang & Hsin-Fu Yeh, 2023. "Baseflow Variation in Southern Taiwan Basin," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    7. Catherine Araujo Bonjean & Alioune N’diaye & Olivier Santoni, 2019. "Who benefits from the return of the rains? The case of the Ferlo breeders in Senegal [A qui profite le retour des pluies ? Le cas des éleveurs du Ferlo]," CERDI Working papers halshs-02419601, HAL.
    8. Roquia Salam & Abu Reza Md. Towfiqul Islam & Shakibul Islam, 2020. "Spatiotemporal distribution and prediction of groundwater level linked to ENSO teleconnection indices in the northwestern region of Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4509-4535, June.
    9. Jing Xu & Ping Zhao & Johnny C. L. Chan & Mingyuan Shi & Chi Yang & Siyu Zhao & Ying Xu & Junming Chen & Ling Du & Jie Wu & Jiaxin Ye & Rui Xing & Huimei Wang & Lu Liu, 2024. "Increasing tropical cyclone intensity in the western North Pacific partly driven by warming Tibetan Plateau," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    10. Sanghyuk Yoo & Sangyong Jeon & Seunghwan Jeong & Heesoo Lee & Hosun Ryou & Taehyun Park & Yeonji Choi & Kyongjoo Oh, 2021. "Prediction of the Change Points in Stock Markets Using DAE-LSTM," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    11. Uilson Ricardo Venâncio Aires & Demetrius David Silva & Michel Castro Moreira & Carlos Antônio Alvares Soares Ribeiro & Celso Bandeira de Melo Ribeiro, 2020. "The Use of the Normalized Difference Vegetation Index to Analyze the Influence of Vegetation Cover Changes on the Streamflow in the Manhuaçu River Basin, Brazil," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 1933-1949, April.
    12. Joseph Ngatchou-Wandji & Echarif Elharfaoui & Michel Harel, 2022. "On change-points tests based on two-samples U-Statistics for weakly dependent observations," Statistical Papers, Springer, vol. 63(1), pages 287-316, February.
    13. Hsin-Yu Chen & Chia-Chi Huang & Hsin-Fu Yeh, 2021. "Quantifying the Relative Contribution of the Climate Change and Human Activity on Runoff in the Choshui River Alluvial Fan, Taiwan," Land, MDPI, vol. 10(8), pages 1-14, August.
    14. Xue Zhong & Xiaohui Jiang & Leilei Li & Jing Xu & Huanyu Xu, 2020. "The Impact of Socio-Economic Factors on Sediment Load: A Case Study of the Yanhe River Watershed," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    15. Dario Camuffo & Antonio della Valle & Francesca Becherini & Valeria Zanini, 2020. "Three centuries of daily precipitation in Padua, Italy, 1713–2018: history, relocations, gaps, homogeneity and raw data," Climatic Change, Springer, vol. 162(2), pages 923-942, September.
    16. Ijaz Ahmad & Li Wang & Faisal Ali & Fan Zhang, 2022. "Spatiotemporal Patterns of Extreme Precipitation Events over Jhelum River Basin," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    17. Yuke Zhou & Junfu Fan & Xiaoying Wang, 2020. "Assessment of varying changes of vegetation and the response to climatic factors using GIMMS NDVI3g on the Tibetan Plateau," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-25, June.
    18. Tweneboah Senzu, Emmanuel, 2020. "Modern currency exchange rate behaviour and proposed trend-like forecasting model," MPRA Paper 99933, University Library of Munich, Germany.
    19. Nathan C. Healey & Jennifer A. Rover, 2022. "Analyzing the Effects of Land Cover Change on the Water Balance for Case Study Watersheds in Different Forested Ecosystems in the USA," Land, MDPI, vol. 11(2), pages 1-43, February.
    20. Solomon Temidayo Owolabi & Johanes A. Belle & Sonwabo Mazinyo, 2022. "Quantifying Intra-Catchment Streamflow Processes and Response to Climate Change within a Climatic Transitional Zone: A Case Study of Buffalo Catchment, Eastern Cape, South Africa," Mathematics, MDPI, vol. 10(16), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2563-:d:343025. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.