SPATIAL MAPPING AND CLUSTER ANALYSIS OF COVID-19: A CASE STUDY OF UTTAR PRADESH, INDIA

Abstract

In public health, the representation and analysis of the incidence of the disease play an important role in assessing the regional disparity and health infrastructure. The geographic information system is one of the best methods to do so. This paper aims to visualise the spread of COVID-19 and perform cluster analysis. The prospective Poisson space-time scan statistic was utilised to detect clusters of COVID-19 at the district level in the Uttar Pradesh state of India. The spatial mapping was performed to assess the situation of COVID-19 and related factors. The log-likelihood ratio and relative risk were calculated monthly from May to December 2020. As per the results, the size and location of clusters kept changing with being more concentrated in Lucknow, Kanpur, Gautam Buddha Nagar districts and NCR regions. The significance of these clusters was less than 0.001. The detection of these clusters helped understand the overall dynamics of the disease spread. The number of confirmed cases declined in most districts, with only a few being at higher risk and needing more attention and resources. It was concluded based on the analysis that the areas of higher economic activities and population density with higher access to hospitals and testing had a larger number of cases and were the regions of hotspots. The findings can help to create health awareness, monitor situations in real-time and evaluate the steps taken to assess their efficacy.