Epidemic Management via Imperfect Testing: A Multi-criterial Perspective

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Diagnostic testing and isolation are crucial for epidemic containment, but imperfect test accuracy (false positives/negatives) complicates their use.
• The COVID-19 pandemic highlighted challenges in balancing public health protection with socioeconomic impacts during large-scale outbreaks.

Purpose of the Study:

• To analyze the trade-offs between diagnostic testing and mandatory isolation for epidemic containment.
• To develop an extended Susceptible-Infected-Recovered (SIR) model incorporating population stratification by test results.
• To identify efficient testing and isolation strategies balancing epidemic control objectives.

Main Methods:

• Extended Susceptible-Infected-Recovered (SIR) epidemiological model.
• Incorporation of population stratification based on diagnostic test outcomes (true positive, false positive, true negative, false negative).
• Multi-criterial framework to evaluate Pareto-efficient testing and isolation scenarios.

Main Results:

• Careful assessment of testing and isolation protocols can contribute to epidemic containment, even with imperfect test accuracy.
• The model demonstrates that optimized strategies can mitigate disease spread despite false negative and false positive results.
• Pareto-efficient scenarios were identified to minimize case counts, reduce isolation duration, or achieve a balance between these objectives.

Conclusions:

• Diagnostic testing and isolation, when strategically implemented, are effective tools for managing epidemic transmission.
• Mathematical modeling provides a framework for optimizing public health interventions in the face of imperfect diagnostic capabilities.
• The study offers insights into balancing public health goals and socioeconomic considerations during infectious disease outbreaks.