Optimal intervention strategies to mitigate the COVID-19 pandemic effects

TL;DR

This paper develops a framework for designing optimal COVID-19 intervention strategies that balance health outcomes and socio-economic costs, considering healthcare capacity, testing, and uncertainties.

Contribution

It introduces a practical approach for selecting and timing policies that are near-optimal, accounting for healthcare and testing factors, and analyzes the impact of uncertainties.

Findings

Healthcare capacity and testing rate significantly influence optimal strategies.

Practical strategies with few policy changes can be nearly optimal.

Uncertainty in parameters affects stricter strategies more.

Abstract

Governments across the world are currently facing the task of selecting suitable intervention strategies to cope with the effects of the COVID-19 pandemic. This is a highly challenging task, since harsh measures may result in economic collapse while a relaxed strategy might lead to a high death toll. Motivated by this, we consider the problem of forming intervention strategies to mitigate the impact of the COVID-19 pandemic that optimize the trade-off between the number of deceases and the socio-economic costs. We demonstrate that the healthcare capacity and the testing rate highly affect the optimal intervention strategies. Moreover, we propose an approach that enables practical strategies, with a small number of policies and policy changes, that are close to optimal. In particular, we provide tools to decide which policies should be implemented and when should a government change to a different policy. Finally, we consider how the presented results are affected by uncertainty in the initial reproduction number and infection fatality rate and demonstrate that parametric uncertainty has a more substantial effect when stricter strategies are adopted.