Optimal Adaptive Testing for Epidemic Control: Combining Molecular and Serology Tests

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• Surveillance testing is crucial for early epidemic control but faces limitations due to cost, invasiveness, and resource scarcity.
• Existing strategies may not efficiently balance testing needs with epidemic containment goals.

Purpose of the Study:

• To determine optimal testing strategies for minimizing resource use while controlling epidemic spread.
• To identify methods for minimizing peak infections within a constrained testing budget.

Main Methods:

• Investigated two optimal control problems using the Susceptible-Infected-Recovered (SIR) model.
• Developed adaptive testing policies dependent on the current epidemic state.
• Utilized the concept of observability to integrate different testing modalities.

Main Results:

• Optimal adaptive testing policies significantly reduce costs compared to non-adaptive strategies.
• Adaptive policies effectively maintain infection numbers below thresholds and minimize peak infections.
• Combining sensitive molecular tests with broader-coverage serology tests improves epidemic state estimation.

Conclusions:

• Adaptive surveillance testing is a more efficient and cost-effective approach to epidemic control.
• Integrating diverse testing methods enhances the ability to manage and predict epidemic trajectories.
• Optimal control strategies can overcome resource limitations in public health surveillance.