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

Area of Science:

• Epidemiology
• Public Health Policy
• Behavioral Economics

Background:

• Early COVID-19 waves saw rapid spread driven by undocumented asymptomatic infections.
• Asymptomatic cases created a 'silent' spread, biasing transmission, prevalence, and fatality rate estimations.
• Lack of economic incentives for testing and isolation among asymptomatic individuals exacerbated disease spread.

Purpose of the Study:

• To analyze the impact of asymptomatic infections and prevalence-dependent testing behavior on COVID-19 dynamics.
• To develop an analytical framework integrating individual testing decisions into epidemiological models.
• To provide policy insights for effective pandemic containment strategies.

Main Methods:

• Developed an analytical epidemiological model incorporating prevalence-dependent testing and isolation behavior.
• Simulated disease transmission dynamics considering the calculus of individual benefits and costs for testing.
• Analyzed the influence of asymptomatic transmission on overall pandemic progression and fatality rates.

Main Results:

• Asymptomatic SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) spread significantly underestimated true incidence and fatality rates.
• Prevalence-dependent testing behavior influences disease transmission and mortality dynamics.
• Failure to account for testing behavior leads to underestimation of infection and fatality rates, particularly at low reported prevalence.

Conclusions:

• Accurate pandemic response requires accounting for asymptomatic spread and individual testing behaviors.
• Underestimation of disease rates due to asymptomatic cases skews containment strategies.
• Enhancing testing capacity is a critical countermeasure for future pandemics like COVID-19.