City-scale Simulation of Covid-19 Pandemic and Intervention Policies using Agent-based Modelling

TL;DR

This paper presents a detailed agent-based simulation model of a city to analyze Covid-19 spread and intervention policies, enabling visualization of pandemic dynamics and policy impact assessment.

Contribution

It introduces a city-scale, detailed agent-based model parameterized for Kolkata, capable of reproducing observed pandemic dynamics and evaluating intervention strategies.

Findings

Model accurately reproduces Kolkata's Covid-19 dynamics

Simulation shows potential impacts of different intervention policies

Provides a tool for pre-assessment of public health measures

Abstract

During the Covid-19 pandemic, most governments across the world imposed policies like lock-down of public spaces and restrictions on people's movements to minimize the spread of the virus through physical contact. However, such policies have grave social and economic costs, and so it is important to pre-assess their impacts. In this work we aim to visualize the dynamics of the pandemic in a city under different intervention policies, by simulating the behavior of the residents. We develop a very detailed agent-based model for a city, including its residents, physical and social spaces like homes, marketplaces, workplaces, schools/colleges etc. We parameterize our model for Kolkata city in India using ward-level demographic and civic data. We demonstrate that under appropriate choice of parameters, our model is able to reproduce the observed dynamics of the Covid-19 pandemic in Kolkata, and also indicate the counter-factual outcomes of alternative intervention policies.