Optimal strategies for pandemic control: balancing adherence, costs and intervention timing

Developing control principles for future pandemics is vital for public health systems. Lessons from COVID-19 highlight the trade-offs between the direct health benefits of social distancing and its broader societal costs, which influence control strategies ranging from elimination to suppression and mitigation. Optimal control offers a valuable framework for navigating these complexities. This presentation explores open-loop optimal control in a COVID-19 transmission model during an aggressive outbreak, focusing on social distancing interventions that balance direct epidemiological impacts with societal costs. Two models are compared: a pre-behavioral model, where adherence is fixed, and a more complex model incorporating adherence dynamics through an evolutionary game equation accounting for fatigue. In the pre-behavioral model, optimal social distancing strategies depend on factors such as the prioritization of societal costs, adherence levels, timing of interventions, and permissible traveler inflow. Increasing emphasis on societal costs shifts the optimal strategy from elimination to suppression and, ultimately, to mitigation. Notably, the "effective" mitigation zone, where hospitals remain within capacity, is quite narrow. The analysis highlights the delicate interplay between public adherence, the timing of government interventions, and the impact of undetected infective travelers. These travelers disrupt strict control by continuously introducing new infections, making complete virus elimination unfeasible. When such inflows are accounted for, the optimal social distancing strategy shifts to one of low to moderate intensity, but extended over a prolonged period, emphasizing the need for sustained, well-timed measures. Low adherence or delayed responses limit viable options, often leaving mitigation as the only feasible strategy. When adherence is endogenized, introducing fatigue enriches the model’s behavior. However, the prioritization of societal costs still determines whether suppression or mitigation is optimal. A key finding is that, under fatigue, optimal strategies are always intermittent, reflecting the dynamic nature of adherence over time. These results emphasize the potential of optimal control as a powerful tool for developing robust pandemic preparedness strategies, which has traditionally been underutilized in public health planning.