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. © 2022 IEEE.

Fine-Grained Prediction and Control of Covid-19 Pandemic in a City: Application to Post-Initial Stages

Predicting the evolution of the Covid-19 pandemic during its early phases was relatively easy as its dynamics were governed by few influencing factors that included a single dominant virus variant and the demographic characteristics of a given area. Several models based on a wide variety of techniques were developed for this purpose. Their prediction accuracy started deteriorating as the number of influencing factors and their interrelationships grew over time. With the pandemic evolving in a highly heterogeneous way across individual countries, states, and even individual cities, there emerged a need for a contextual and fine-grained understanding of the pandemic to come up with effective means of pandemic control. This paper presents a fine-grained model for predicting and controlling Covid-19 in a large city. Our approach borrows ideas from complex adaptive system-of-systems paradigm and adopts a concept of agent as the core modeling ion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.