Segmentized quarantine policy for managing a tradeoff between containment of infectious disease and social cost of quarantine

Abstract

By the end of 2021, COVID-19 had spread to over 230 countries, with more than 5.4 million deaths. To contain the disease spread, many countries have deployed non-pharmaceutical intervention strategies, most notably contact tracing and self-quarantine policy. We have observed that containment of disease spread by such social distancing policy come at a large social cost, and prolonged pandemic raised the necessity of more sustainable policy with the least disruption to economic and societal activities. This research aims to investigate a segmentized quarantine policy where we apply different quarantine policies for different population segments with a goal of better managing the tradeoff between the benefit and cost of a quarantine strategy. Motivation for a segmentized policy is that different population groups, e.g., school students vs adults with jobs, exhibit different patterns of societal activities, thereby imposing different risks to disease spread. We define a segmentized quarantine policy in two dimensions - range of contact tracing and quarantine period, and determine the two parameters for each population segment to achieve two objectives: to minimize the total number of infected cases and to minimize the total days of self-quarantine. We use Agent-based Epidemics Simulation to evaluate the quarantine policies, and Evolutionary Algorithm is used to obtain the Pareto front of our problem. Our results demonstrate the effectiveness of the segmentized quarantine policies, and we identify the conditions where they outperform the uniform policy. We also find in the Pareto optimal solutions that only some population segments are recommended special policy features while other segments are subject to the conventional policy. The results suggest that segmentized quarantine policy is valid in terms of efficiency and sustainability, and the suggestions and framework presented are expected to be of great help in establishing public health decisions to prepare for an upcoming pandemic like COVID-19.