Planning interventions in a controlled pandemic: the COVID-19 case

TL;DR

This paper analyzes control strategies for managing COVID-19, focusing on infection rate and hospitalization controls, considering uncertainties, delays, and economic impacts to inform policymaker decisions during a pandemic.

Contribution

It introduces a framework to evaluate control strategies under uncertainty, delays, and population heterogeneity, incorporating vaccination and economic considerations.

Findings

Controlling infection rate is challenging due to information noise and instability.

Hospitalization-based control is more stable but affected by delays.

The framework assesses social and economic costs of different strategies.

Abstract

Restrictions on social and economic activities, as well as vaccinations, have been a key intervention in containing the COVID-19 epidemic. Our work focuses on better understanding the options available to policymakers under the conditions and uncertainties created by the onset of a new pandemic. More precisely, we focus on two control strategies. The first aims to control the rate of new infections to prevent congestion of the health care system. The latter directly controls hospitalizations and intensive care units (ICUs) occupation. By a first-order analysis, we show that, on the one hand, due to the difficulty in contact tracing and the lack of accurate information, controlling the transmission rate may be difficult, leading to instability. On the other hand, although hospitalizations and ICUs are easily accessible and less noisy than the rate of new infections, a delay is introduced in the control loop, which may endanger system stability. Our framework allows assessing the impact on economic and social costs of the above strategies in a scenario enriched by: i) population heterogeneity in terms of mortality rate and risk exposure, ii) closed-loop control of the epidemiological curve, and iii) progressive vaccination of individuals.