AGENT BASED SIMULATABLE CITY DIGITAL TWIN TO EXPLORE DYNAMICS OF COVID-19 PANDEMIC

Predicting the evolution of Covid19 pandemic has been a challenge as it is significantly influenced by the characteristics of people, places and localities, dominant virus strains, extent of vaccination, and adherence to pandemic control interventions. Traditional SEIR based analyses help to arrive at a coarse-grained ‘lumped up’ understanding of pandemic evolution which is found wanting to determine locality-specific measures of controlling the pandemic. We comprehend the problem space from system theory perspective to develop a fine-grained simulatable city digital-twin for “in-silico” experimentations to systematically explore - Which indicators influence infection spread to what extent? Which intervention to introduce, and when, to control the pandemic with some a-priori assurance? How best to return to a new normal without compromising individual health safety? This paper presents a digital twin centric simulation-based approach, illustrates it in a real-world context of an Indian City, and summarizes the learning and insights based on this experience.