Spatio-temporal analysis of COVID-19 in India – a geostatistical approach

Abstract

Coronavirus (Covid) is a severe acute respiratory syndrome infectious disease, spreads primarily between human beings during close contact, most often through the coughing, sneezing, and speaking small droplets. A retrospective surveillance research is conducted in India during 30th January–21st March 2020 to gain insight into Covid’s epidemiology and spatial distribution. Voronoi statistics is used to draw attention of the affected states from a series of polygons. Spatial patterns of disease clustering are analyzed using global spatial autocorrelation techniques. Local spatial autocorrelation has also been observed using statistical methods from Getis-Ord Gi*. The findings showed that disease clusters existed in the area of research. Most of the clusters are concentrated in the central and western states of India, while the north-eastern countries are still predominantly low-rate of clusters. This simulation technique helps public health professionals to identify risk areas for disease and take decisions in real time to control this viral disease.