To test or not to test? A new behavioral epidemiology framework for COVID-19

Recent clinical research finds that rapid transmission of SARS-CoV-2 is facilitated by substantial undocumented asymptomatic infections. Asymptomatic infections have implications for behavioral response to voluntary testing. The paper argues that a substantial proportion of SARS-CoV-2 infections are hidden due to rational test avoidance behavior, especially among those without perceptible disease symptoms. However, if perception of disease threat is prevalence dependent, testing compliance increases in response to reported infection prevalence rate in the population. This behavior, in turn, affects infection and mortality dynamics. This paper proposes an analytical framework that explicitly incorporates prevalence-dependent testing behavior in a standard epidemiological model, generating distinctive equilibrium epidemiological outcomes with significant policy implications. Numerical simulations show that failure to consider endogenous testing behavior among asymptomatic individuals leads to over- and underestimation of infection rates at the peaks and troughs, respectively, thereby distorting the disease containment policies. The results underscore the importance of augmenting testing capacity as an effective mitigation policy for COVID-19 and similar infectious diseases.