Containing COVID-19 Pandemic using Community Detection

Abstract

Abstract Diseases such as COVID-19 that quickly spread through social contact where Infections remain undetected for long pose new kinds of challenges. Traditional epidemic models when applied to COVID-19 not only give highly alarmist predictions but also fail to indicate any way to target interventions. The models also do not factor social structure/context/dynamics.. To address this challenge we propose a novel Social Infection Analysis Model explain the intuition behind it and a methodology that factors social structure while performing the analysis. SIAM is particularly beneficial as standard epidemic models are dependent on wide-spread testing. Our methodology includes (i) modelling society/localities using social network paradigm and analysis at macro level (ii) detecting communities and (iii) analysis of infection spread within a community over time. To arrive at an outlook for infection spread within communities we make use of the NK Model for solving complex, long term problems coupled with agent based modeling for member behavior. We make use of NetLogo to run simulations. We intend to validate our research in Indian context.