Assessment of event-triggered policies of nonpharmaceutical interventions based on epidemiological indicators

TL;DR

This paper introduces a mathematical framework to evaluate and compare different epidemiological indicators for triggering nonpharmaceutical interventions during pandemics, aiming to optimize outcomes like ICU demand and lockdown duration.

Contribution

It proposes a novel methodology to assess and select the most effective epidemiological indicators for event-triggered policies using a curve dominance approach.

Findings

Framework successfully compares indicators based on trade-offs between outcomes.

Method applied to COVID-19 data with compartmental models.

Guides optimal indicator selection for intervention policies.

Abstract

Nonpharmaceutical interventions (NPI) such as banning public events or instituting lockdowns have been widely applied around the world to control the current COVID-19 pandemic. Typically, this type of intervention is imposed when an epidemiological indicator in a given population exceeds a certain threshold. Then, the nonpharmaceutical intervention is lifted when the levels of the indicator used have decreased sufficiently. What is the best indicator to use? In this paper, we propose a mathematical framework to try to answer this question. More specifically, the proposed framework permits to assess and compare different event-triggered controls based on epidemiological indicators. Our methodology consists of considering some outcomes that are consequences of the nonpharmaceutical interventions that a decision maker aims to make as low as possible. The peak demand for intensive care units (ICU) and the total number of days in lockdown are examples of such outcomes. If an epidemiological indicator is used to trigger the interventions, there is naturally a trade-off between the outcomes that can be seen as a curve parameterized by the trigger threshold to be used. The computation of these curves for a group of indicators then allows the selection of the best indicator the curve of which dominates the curves of the other indicators. This methodology is illustrated using indicators in the context of COVID-19 using deterministic compartmental models in discrete-time, although the framework can be adapted for a larger class of models.