Disease-structured N-mixture models: A practical guide to model disease dynamics using count data.

Area of Science:

• Quantitative ecology
• Disease ecology
• Statistical modeling

Background:

• Estimating wildlife disease dynamics often requires mark-recapture methods, which are challenging for large populations or those with low survival rates.
• Virulent pathogens can decimate host populations, leading to low recapture rates that preclude traditional mark-recapture analyses.

Purpose of the Study:

• To review challenges in modeling disease dynamics.
• To describe how N-mixture models can estimate key disease metrics.
• To account for imperfect detection of hosts and pathogens.

Main Methods:

• N-mixture models, a type of state-space model, attribute observation error to false negatives (undetected individuals).
• The approach uses repeated surveys over a closed population period to estimate detection probability.
• Models are adapted to estimate pathogen prevalence, transmission, and recovery rates.

Main Results:

• N-mixture models offer a robust statistical framework for estimating wildlife disease dynamics from count data.
• These models can account for imperfect detection, a common issue in ecological studies.
• The framework facilitates estimation of crucial disease parameters like prevalence and transmission rates.

Conclusions:

• N-mixture models are a valuable tool for disease ecology, especially when traditional methods are infeasible.
• Future research should explore false positive estimation, spatial modeling, and data integration.
• Accurate disease dynamic estimates are vital for biodiversity conservation and managing emerging infectious diseases.