Understanding Emergent Dynamism of Covid-19 Pandemic in a City

Predicting the evolution of a pandemic requires precise understanding of the pathogen and disease progression, the susceptible population group, means of transmission, and possible control mechanisms. It has been a significant challenge as Covid-19 virus (SARS-CoV-2 family) is not well understood yet; the entire human population is susceptible, and the virus transmits easily through airborne particles. Given its size and connectedness, it is not feasible to test the entire population and to isolate the infected individuals. Moreover, rapid and continuous mutation of virus open up the possibility of reinfection. As a result, the evolution of pandemic is not uniform and in-step throughout the world but is significantly influenced by local characteristics pertaining to people, places, dominant virus strain, extent of vaccination, and adherence to pandemic control interventions. Traditional macro-modelling techniques, such as variations of SEIR models, provide only a coarse-grained, 'lumped up' understanding of the pandemic which is not enough for exploring and understanding possible fine-grained factors that are effective for controlling the Covid-19 pandemic. This paper explores the problem space from a system theoretic perspective and presents a fine-grained city digital twin as an in-silico experimentation aid to understand the complex interplay of factors that influence infection spread and also help in controlling the Covid-19 pandemic. Our focus is not to speculate the possibility of the next wave or how the next wave may look like. Instead, we systematically seek answers to questions such as: what are indicators should we consider for a future wave? What are the parameters that may influence those indicators? When and why should they be tweaked (in terms of interventions) to control unacceptable situations? We validate our approach on the second and third waves of Covid-19 pandemic in Pune city.