Optimal Control of Covid-19 Interventions in Public Health Management

TL;DR

This paper applies optimal control theory to develop effective, feasible, and cost-efficient COVID-19 intervention strategies using a novel disease model, aiming to guide policymakers in pandemic management.

Contribution

It introduces a new compartmental model and a framework for optimizing COVID-19 interventions considering cost, effectiveness, and feasibility.

Findings

Optimal strategies balance health benefits and economic costs.

Interventions tailored to feasibility constraints improve implementation.

The model provides actionable insights for policymakers.

Abstract

This study explores the application of Pontryagin's Maximum Principle to derive optimal strategies for controlling the spread of COVID-19, leveraging a novel compartmental model to capture the disease dynamics. We prioritize three key criteria: cost, effectiveness, and feasibility, each examined independently to evaluate their unique contributions to pandemic management. By addressing these criteria, this study aims to design intervention strategies that are scientifically robust, practical, and economically sustainable. Furthermore, the focus on cost, effectiveness and feasibility seeks to provide policymakers with actionable insights for implementing interventions that maximize public health benefits while remaining feasible under real-world conditions.