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

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.