The cost of myopic pandemic response

Prior to the availability of COVID-19 vaccines, non-pharmaceutical interventions (NPIs) served as a primary strategy to mitigate the spread of the disease. However, the efficiency of these interventions relies on understanding and incorporating human behavior into infectious disease models. This study addresses the need for models that better account for the influence of temporal discounting on behavioral dynamics to enhance forecasting accuracy and develop robust mitigation strategies. Our previous research introduced Known Time Horizon (KTH) policies, optimizing social distancing measures based on a central planners rational assessment of the pandemics time frame and associated costs. In this paper, we contrast the KTH policy with a model reflecting myopic decision-making, an extreme form of temporal discounting that emphasizes short-term outcomes over long-term consequences. By comparing the expected social distancing behavior under myopic decision-making with the optimal policy derived from KTH approaches, we elucidate the impact of temporal bias on social distancing practices and assess its implications for infection dynamics and associated costs. We find that myopic policy always results in greater total costs throughout an epidemic compared to a KTH policy. However, each cost component - the costs of infection and social distancing - derived from a myopic strategy may be either larger or smaller than the component costs for a strategy developed using a full optimization model, depending on the specific parameters involved as myopic decision-makers seek to delay both costs of social distancing and infection.