An Agent-Based Digital Twin for Exploring Localized Non-pharmaceutical Interventions to Control COVID-19 Pandemic

Abstract

The COVID-19 epidemic created, at the time of writing the paper, highly unusual and uncertain socio-economic conditions. The world economy was severely impacted and business-as-usual activities severely disrupted. The situation presented the necessity to make a trade-off between individual health and safety on one hand and socio-economic progress on the other. Based on the current understanding of the epidemiological characteristics of COVID-19, a broad set of control measures has emerged along dimensions such as restricting people's movements, high-volume testing, contract tracing, use of face masks, and enforcement of social-distancing. However, these interventions have their own limitations and varying level of efficacy depending on factors such as the population density and the socio-economic characteristics of the area. To help tailor the intervention, we develop a configurable, fine-grained agent-based simulation model that serves as a virtual representation, i.e., a digital twin of a diverse and heterogeneous area such as a city. In this paper, to illustrate our techniques, we focus our attention on the Indian city of Pune in the western state of Maharashtra. We use the digital twin to simulate various what-if scenarios of interest to (1) predict the spread of the virus; (2) understand the effectiveness of candidate interventions; and(3) predict the consequences of introduction of interventions possibly leading to trade-offs between public health, citizen comfort, and economy. Our model is configured for the specific city of interest and used as an in-silico experimentation aid to predict the trajectory of active infections, mortality rate, load on hospital, and quarantine facility centers for the candidate interventions. The key contributions of this paper are: (1) a novel agent-based model that seamlessly captures people, place, and movement characteristics of the city, COVID-19 virus characteristics, and primitive set of candidate interventions, and(2) a simulation-driven approach to determine the exact intervention that needs to be applied under a given set of circumstances. Although the analysis presented in the paper is highly specific to COVID-19, our tools are generic enough to serve as a template for modeling the impact of future pandemics and formulating bespoke intervention strategies.

Understanding Emergent Dynamism of Covid-19 Pandemic in a City

“… 2020 ) were significantly off the mark in terms of their ability to predict the trends in infection count, critical cases, and fatality—including ours (Barat et al. 2021 ). We were successful in predicting the timeline of the second wave in the Indian city of Pune 1 several months before the wave started; however, our prediction of the amplitude of peak cases was lower than reality.…”

“…Chiefly these coarse-grained models ignore the heterogeneity of the population in terms of age, comorbidity, and socio-economic factors that manifest in wide variance of individual behaviours of the population (Barat et al. 2021 ; Kerr et al. 2021 ), as they focus on aggregated movement of population from one cohort to other.…”

“…During the first wave (March–November 2020) in India, we constructed a parameterized city digital twin (Barat et al. 2021 ) to explore the efficacy of non-pharmaceutical interventions such as movement restrictions and relaxations, testing, mask usage. Technically, a fine-grained agent modelling technique was effectively leveraged to faithfully represent four aspects of interest that were believed to be the key influencers of the Covid-19 infection spread during the first wave: (a) epidemiological aspects of the Covid-19 virus ( i.e.…”

“…We conducted this ex post facto analysis using a comprehensive parameterized digital twin that extends our earlier digital twin of Pune (Barat et al. 2021 ) and by critically revisiting the actual situation in Pune from January 2021 to November 2021 (essentially the period that covers the entire second wave). Conceptually, we combined a data-centric approach with a domain model–centric approach accompanied by rigorous simulation-based experimentation.…”

AI-driven streamlined modeling: experiences and lessons learned from multiple domains

“…We request the reader to refer to [12] for the metamodel of a complex system of actors as well as the formal representation of the RL agent's interaction with the real system. Additional application of actionable what-if analyses enabled using RL includes scenario playing for research ranking improvement for a university [11], and a nontrivial exploration of localized non-pharmaceutical interventions to control COVID-19 [13].…”