Simulation of pooling optimization methods for differing infection dynamics, sampling practices, and desired outcomes...

Area of Science:

• Veterinary epidemiology
• Diagnostic assay optimization
• Infectious disease surveillance

Background:

• Pooling strategies in disease surveillance aim to optimize testing economy and reduce costs.
• The reliability of prevalence assumptions for pooling optimization varies significantly across pathogens and surveillance designs.
• Suboptimal pooling due to unknown or unpredictable prevalence can lead to increased surveillance program costs.

Purpose of the Study:

• To evaluate the performance of different pooling optimization methods in maximizing testing economy and cost reduction.
• To compare the effectiveness of various methods for approximating optimal pool size (OPS) across different surveillance programs.
• To assess the compatibility of pooling optimization methods with surveillance priorities, sampling practices, and pathogen infection dynamics.

Main Methods:

• Simulated different pooling optimization methods using Monte Carlo simulations.
• Utilized data from bovine viral diarrhea virus (BVDV) and *Theileria orientalis* surveillance submissions.
• Determined true prevalence, OPS, and historical prevalence for each submission.
• Trialed fixed pool sizes, historical prevalence, and prevalence estimation testing methods.

Main Results:

• Contrasting results were observed between BVDV and *T. orientalis* surveillance.
• Historical prevalence was the most reliable optimization method for BVDV.
• *T. orientalis* surveillance required significantly more tests with historical prevalence compared to truly optimized pooling (p <0.05).

Conclusions:

• The selection of a pooling optimization approach must consider the interplay of pathogen infection dynamics, sampling practices, and surveillance program priorities.
• No single pooling optimization method is universally superior; context-specific evaluation is necessary.
• Accurate prevalence estimation is critical for effective and economical disease surveillance.