The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and...

Area of Science:

• Epidemiology
• Public Health Informatics
• Network Science

Background:

• Infectious disease surveillance systems are essential for public health policy.
• Current surveillance network design often relies on expert opinion rather than formal optimization.
• Limited resources necessitate efficient and effective surveillance network design.

Purpose of the Study:

• To propose a simulation framework for evidence-based infectious disease surveillance network design.
• To optimize surveillance networks for specific public health goals under operational constraints.
• To address the need for formal analysis in surveillance system design.

Main Methods:

• Developed the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework.
• Utilized mathematical modeling of disease and surveillance processes.
• Employed numerical optimization techniques to identify optimal network designs.

Main Results:

• Demonstrated how optimal surveillance network design is sensitive to specific goals (e.g., spatial prediction, risk factor estimation).
• Showcased the impact of underlying disease spatial patterns on network effectiveness.
• Illustrated the value of quantitative and adaptive analysis for network performance.

Conclusions:

• Quantitative, adaptive analysis of network characteristics is vital for designing effective surveillance systems.
• The DIOS framework can be tailored to specific disease dynamics and surveillance needs.
• This approach improves understanding of trade-offs in surveillance network architecture.