Spatio-temporal Variances of COVID-19 Active Cases and Genomic Sequence Data in India

Abstract

Active cases of the COVID-19 pandemic have been reported for more than a year, and separately, there have been significant efforts to collect genome sequencing data during this period to track mutations and evolving strains. While both these datasets can be independently analyzed over space and time, the pattern and variances as evidenced by clustering of these datasets during two different waves of the epidemic in India show important differences. Quantification of these differences can help characterize relative need for collection of genomic data. Differences in the clusters are evident both spatially and temporally, and there are varying distances between such clusters as well. While similarity metrics and techniques have been developed in the context of spatio-temporal datasets, especially in moving objects, we demonstrate the limitations of such methods in analyzing epidemiological data. Finally, we highlight the challenges of such analysis in massive datasets and performance constraints at variant spatial and temporal scales.