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

Abstract

Evidence from clinical research suggests that in the first two waves of COVID-19, the virus spread rapidly through a large number of undocumented asymptomatic infections. These ‘silent’ infections camouflaged the actual incidence of the disease, leading to downward biases in the rates of transmission, disease prevalence, and fatality. These, in turn, had implications for how people and policymakers responded to changing infection prevalence. This paper posits that in the early stages of the COVID-19 pandemic, a considerable number of SARS-CoV-2 infections spread through asymptomatic infected individuals who lacked economic incentives to test and isolate adequately. The decision to undertake testing and the subsequent possibility of isolation entails a calculus of benefits and costs for an individual. Given that the perceived net benefit of such actions is correlated with the observed risk of infection, the likelihood of an asymptomatic individual choosing to undergo testing increases with the existing infection prevalence rate. This behavior, in turn, influenced disease transmission and mortality dynamics. This study presents an analytical framework that integrates prevalence-dependent testing behavior into a traditional epidemiological model. The model’s predictions provide critical policy insights. It reveals that failing to account for testing and isolation behavior results in underestimation of the infection propagation and fatality rates when reported disease prevalence is low, thereby, skewing the containment strategies in the initial and late stages of a pandemic. The findings underscore the necessity of enhancing testing capacity as a crucial countermeasure for future contagions like COVID-19.