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

Brototi Biswas (), Ketan Das, Debashis Saikia and Pradip Chouhan
Additional contact information
Brototi Biswas: Mizoram University
Ketan Das: Mizoram University
Debashis Saikia: Gauhati University
Pradip Chouhan: University of GourBanga

Sankhya A: The Indian Journal of Statistics, 2024, vol. 86, issue 1, No 1, 26 pages

Abstract: 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.

Keywords: COVID-19; North-eastern India; Getis-ord Gi*; Hotspot analysis; SEIR (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13171-023-00312-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-023-00312-y

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13171

DOI: 10.1007/s13171-023-00312-y

Access Statistics for this article

Sankhya A: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya A: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().