Artificial intelligence framework for threat assessment and containment for covid-19 and future epidemics while mitigating the socioeconomic impact to women, children, and underprivileged groups

With the emergency situation that arises with COVID-19, the intense containment strategies adopted by many countries had little or no consideration towards socio-economic ramifications or the impact on women, children, socio­economically underprivileged groups. The existence of many adverse impacts raises questions on the approaches taken and demands proper analysis, scrutiny and review of the policies. Therefore, a framework was developed using the artificial intelligence (Al) techniques to detect, model, and predict the behaviour of the COVID-19 pandemic containment strategies, understanding the socio-economic impact of these strategies on identified diverse vulnerable groups, and the development of AI-based solutions, to predict and manage a future spread of COVID or similar infectious disease outbreaks while mitigating the social and economic toil. Based on generated behaviour and movements, Al tools were developed to conduct contact tracing and socio-economic impact mitigation actions in a more informed, socially conscious and responsible manner in the case of the next wave of COVID-19 infections or a different future infectious disease. © 2022, National Science Foundation. All rights reserved.

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

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.