IDEAS home Printed from https://ideas.repec.org/a/spr/sankha/v86y2024i1d10.1007_s13171-023-00312-y.html
   My bibliography  Save this article

COVID-19 Hotspot Mapping and Prediction in Aizawl District of Mizoram: a Hotspot and SEIR Model-Based Analysis

Author

Listed:
  • Brototi Biswas

    (Mizoram University)

  • Ketan Das

    (Mizoram University)

  • Debashis Saikia

    (Gauhati University)

  • Pradip Chouhan

    (University of GourBanga)

Abstract

The COVID-19 virus rapidly expanded worldwide and infected people from most of the countries (215) within a span of three months. The virus did not spare even the remote geographical areas, including the remote regions of India’s hilly north-eastern states. According to the Ministry of Health and Family Welfare (MoHFW, GoI), report 2021, the recovery rate of the Aizawl district was very low, but the positivity rate was high during the second wave of this pandemic, compared to the national average. Hence, the present work is aimed at analysing the spatial pattern of COVID-19 in Mizoram through hotspot analysis and forecasting the trend of coronavirus spread using the susceptible-exposed-infected-removed (SEIR) model. To show the clustering pattern of COVID-19 in Aizawl we used Getis-Ords Gi* statistic. The Getis-Ords Gi* statistic defines a cluster of values that are higher or lower than expected by chance giving the output as a z score.Getis-Ords Gi* statistic, also known as “hotspots” and “coldspots”, identify the clustering pattern of high and low values in a spatial distribution.To perform the Getis-Ords Gi* statistic the authors used the monthly average of COVID-19 data for the study period.During the study done between September 2021 and March 2022, hotspot analysis identified the city areas as hotspot zones, while the periphery of city limits was identified as coldspot zones. The forecast was made for 45 days (from July 27th to September 10th, 2022). An ROC curve has been used to validate the prediction result. The area under the curve (AUC) is 76.71%, signifying the validation of the prediction. This research will assist policymakers and the government in developing health management policies to mitigate the effects of a future pandemic.

Suggested Citation

  • Brototi Biswas & Ketan Das & Debashis Saikia & Pradip Chouhan, 2024. "COVID-19 Hotspot Mapping and Prediction in Aizawl District of Mizoram: a Hotspot and SEIR Model-Based Analysis," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 1-26, February.
  • Handle: RePEc:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-023-00312-y
    DOI: 10.1007/s13171-023-00312-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13171-023-00312-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13171-023-00312-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-023-00312-y. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.