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

TL;DR

This paper proposes a segmentized quarantine policy that tailors measures to different population groups, optimizing for reduced infections and social costs during pandemics.

Contribution

It introduces a novel framework for segmentized quarantine, optimizing contact tracing and quarantine durations per group using simulation and evolutionary algorithms.

Findings

Segmentized policies outperform uniform measures in simulations.

Tailored quarantine reduces total infections and quarantine days.

Framework supports sustainable pandemic management.

Abstract

By the end of 2021, COVID-19 had spread to over 230 countries, with over 5.4 million deaths. To contain its spread, many countries implemented non-pharmaceutical interventions, notably contact tracing and self-quarantine policies. However, these measures came with significant social costs, highlighting the need for more sustainable approaches that minimize disruptions to economic and societal activities. This research explores a segmentized quarantine policy, applying different quarantine measures for various population segments to better balance the benefits and costs of containment. Different groups, like students versus working adults, have distinct societal activity patterns, posing varied risks for disease spread. We define segmentized quarantine policy across two dimensions-contact tracing range and quarantine period-and optimize these parameters for each segment to minimize total infection cases and quarantine days. Using an Agent-Based Epidemic Simulation and an Evolutionary Algorithm to derive the Pareto front, we demonstrate that segmentized policies can be more effective than uniform policies, with specific segments benefiting from tailored measures. The findings support segmentized quarantine as a viable, efficient, and sustainable approach, offering a valuable framework for public health policy in future pandemics.