A statistical framework for the adaptive management of epidemiological interventions

Area of Science:

• Epidemiology
• Public Health
• Mathematical Modeling

Background:

• Epidemiological interventions are crucial for controlling infectious disease spread.
• Different intervention strategies incur varying costs.
• Optimizing these interventions is essential for public health resource allocation.

Purpose of the Study:

• To develop a flexible statistical framework for optimal epidemiological interventions.
• To minimize the total expected cost of an emerging epidemic.
• To incorporate uncertainty in disease model parameters into decision-making.

Main Methods:

• A statistical framework was developed to generate optimal, adaptive epidemiological interventions.
• The framework propagates uncertainty in disease model parameters.
• Vaccination schedules are iteratively adjusted based on epidemic trajectory and parameter estimates.

Main Results:

• Simulation studies using an influenza outbreak model were conducted.
• Adaptive interventions demonstrated advantages over non-adaptive strategies.
• Cost and resource efficiency were improved with adaptive interventions.

Conclusions:

• Adaptive epidemiological interventions are more cost-effective and resource-efficient.
• Adaptive strategies offer greater robustness to model misspecification.
• This framework supports optimized public health responses to epidemics.