Optimizing infectious disease mitigation under dynamic conditions

TL;DR

This paper presents a versatile optimization framework for infectious disease mitigation, revealing counterintuitive effects of strictness and timing, and highlighting the importance of timely responses during pandemics and seasonal variations.

Contribution

The authors develop a novel, general optimization model that captures complex trade-offs in infectious disease mitigation, providing new insights into optimal strategies under various conditions.

Findings

Optimal mitigation is either strict or none, with no intermediate levels being optimal.

Seasonal variations lead to stricter mitigation during winter and delayed infection waves.

Even optimal mitigation can cause transient infection waves during vaccination campaigns.

Abstract

Mitigation measures are essential for controlling the spread of infectious diseases during pandemics and epidemics, but they impose considerable societal, individual, and economic costs. We developed a general optimization framework to balance costs related to infection and to mitigation. Optimizing the trade-off between mitigation and infection cost, we identified three novel, surprising effects: First, assuming a constant reproduction number $R_0$, the optimal response to an infectious disease requires either strict mitigation or none at all, depending on disease severity, but never does one find an intermediate mitigation level to be optimal. Second, under seasonal variations, optimal mitigation is stricter during winter. Interestingly, a single wave of infections still arises in spring with 3 months delay to the seasonal peak of infectivity, replacing the autumn/winter waves known for classical influenza. Third, during steady vaccination campaigns, even optimal mitigation can result in transient infection waves. Finally, we quantify the cost of delayed mitigation onset and show that even short delays can substantially increase total costs -- if the disease is severe. Overall, our framework is easily applicable to general and complex settings and thereby presents a versatile tool to explore optimal mitigation strategies for endemic and pandemic infectious disease.