Events in MathSys and Complexity Science

Silent Spreaders: Behaviour and Equilibrium Under Asymptomatic Infection

This paper analyzes equilibrium social distancing choices in a model with potentially asymptomatic infection. Since infection only prompts symptoms probabilistically, individuals cannot perfectly infer their health state from the absence of symptoms. Instead, they must form beliefs about their health state based on knowledge of the population frequencies. I show that relative to a benchmark with perfect health state information, asymptomatic infection leads to lower mitigation through four distinct channels, some mechanistic and some that work through beliefs and thus decisions. The model is then applied to an analysis of individual and mass testing. The value of the former derives from the value of information and it is shown that the latter may influence the course of the epidemic through its influence on aggregate equilibrium behaviour. Tests for immunity generally have a higher value of information and aggregate effects than tests for infection.