Covid-19 Hazard Index: A Spatiotemporal Risk Forecast Tool
Keywords:Geomapping, Spatial analysis, Principal Component Analysis, Weekly Growth rate, Population density, COVID19 vaccine coverage
Background: In a given geographic region, risk of new cases of COVID19 are driven by internal factors such as agent, host and environment characteristics, as well as external factors, such as population mobility and cross border transmission of disease. COVID19 control measures are best implemented when local governments and health teams are well aware of these internal and external risks. These risks are dynamic in nature and hence need to be reviewed at regular intervals. Objective: To develop a composite spatiotemporal Hazard Index comprising of three factors – presence of susceptible population, population density and presence of active cases with corresponding growth rates, to rank areas within an administrative boundary by their fortnightly risk of active COVID19 cases.
Methods: Using Principal Component Analysis, the weights of each of these factors were determined and applied to transformed values of factors in the districts of Gujarat state for months of January to July 2021. Hazard Index thus obtained was used to rank the districts.
Results: Spearman correlation between the Hazard Index and number of active cases 15 days later was moderate and significant (p<0.01) throughout the study period.
Conclusion: Hazard Index can predict Districts at highest risk of active cases in the given time period. These districts with high Hazard Index would require different control measures, depending on the factor that resulted in higher index value.
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Copyright (c) 2022 Manvendra Singh Rathore, Samudyatha U.C., J.K. Kosambiya
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