Behavioral risk compensation and the efficacy of nonpharmacological interventions

The COVID-19 pandemic has forced governments around the world into drastic measures without the normal evidence base or analyses of consequences. We present a quantitative model that can be used to rapidly assess the introduction and interaction of nonpharmaceutical infection prevention measures (NPI) both in rapid a priori predictions and in real-world a posteriori evaluations. Two of the most popular NPIs are imposing minimum physical interpersonal distancing and the use of face coverings. The success of both measures is highly dependent on the behavior of the public. However, there is very little published information about the interactions between distance, mask wearing, and the behavioral adaptations that they are likely to generate. We explore the relation between these two fundamental NPIs and the behavioral responses that they may induce, considering both risk compensation and social norms enhancement. At present, we do not have the necessary information to parameterize our model to a sufficient degree to generate quantitative, immediately applicable, advice, but we explore a vast parameter space and illustrate how the consequences of such measures can range from highly beneficial to paradoxically harmful in plausible real situations.