Investigating dynamics of COVID-19 spread and containment with agent-based modeling

Abstract

Governments, policy makers and officials around the globe are trying to mitigate the effects and progress of the COVID-19 pandemic by making decisions which will save the most lives and impose the least costs. Making these decisions needs a comprehensive understanding about the dynamics by which the disease spreads. In this work, we propose an epidemic agent-based model that simulates the spread of the disease. We show that the model is able to generate an important aspect of the pandemic: multiple waves of infection. A key point in the model description is the aspect of ’fear’ which can govern how agents behave under different conditions. We also show that the model provides an appropriate test-bed to apply different containment strategies and this work presents the results of applying two such strategies: testing, contact tracing, and travel restriction. The results show that while both strategies could result in flattening the epidemic curve and significantly reduce the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.

Policy and behavioral response to shock events: An agent-based model of the effectiveness and equity of policy design features

“…ABMs have also been used in the simulation of previous epidemics such as smallpox [61], H5N1 influenza [62] and H1N1 influenza [63,64]. In the context of COVID-19 policy design features, ABMs have been developed to explore the overall effect of travel restrictions [65], testing and contact tracing [66], and timing and duration of social distancing measures [67,68]. Some of these studies also highlight the importance of population level compliance to the social distancing guidelines [66,67].…”

Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children

“…The COVID-19 crisis is also the result of interacting agents. Therefore, apart from studying human mobility, research engineers have extensively used agent-based modelling to understand the COVID-19 crisis and its implications, predict its future outcomes [59][60][61][62], and make decisions for measures to be taken, such as social distancing interventions [63], reducing transmission in facilities [64], and comparing different policies [65].…”