Spatial analysis of COVID-19 and socio-economic factors in Sri Lanka

TL;DR

This study employs clustering techniques on COVID-19 and socio-economic data in Sri Lanka to identify district-level patterns, aiding policymakers in designing targeted preventive measures during the pandemic.

Contribution

It introduces a novel district-wise clustering approach using K-Means and spectral clustering to analyze COVID-19 spread in relation to socio-economic factors in Sri Lanka.

Findings

Population density and education level strongly correlate with COVID-19 clusters.

Certain districts show high similarity in socio-economic factors and COVID-19 spread.

The approach can inform targeted policy interventions.

Abstract

The spread of the global COVID-19 pandemic affected Sri Lanka similar to how it affected other countries across the globe. The Sri Lankan government took many preventive measures to suppress the pandemic spread. To aid policy makers in taking these preventive measures, we propose a novel district-wise clustering based approach. Using freely available data from the Epidemiological Department of Sri Lanka, a cluster analysis was carried out based on the COVID-19 data and the demographic data of districts. K-Means clustering and spectral clustering models were the selected clustering techniques in this study. From the many district-wise socio-economic factors, population, population density, monthly expenditure and the education level were identified as the demographic variables that exhibit a high similarity with COVID-19 clusters. This approach will positively impact the preventive measures suggested by the relevant policy making parties of the Sri Lankan government.