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Data-Driven Risk Assessment from Small Scale Epidemics: Estimation and Model Choice for Spatio-Temporal Data with
Kokouvi Gamado1, Glenn Marion1, Thibaud Porphyre2
1Biomathematics and Statistics Scotland , Edinburgh , UK.
Frontiers in Veterinary Science
|March 16, 2017
Summary
This study introduces a spatial risk assessment framework for livestock epidemics using historical outbreak data. Latent residual methods reliably select transmission models for accurate risk prediction, even with small outbreaks.
Area of Science:
- Veterinary epidemiology
- Spatial statistics
- Disease modeling
Background:
- Livestock epidemics cause significant economic and social costs.
- Emerging pathogens can lead to small, frequent outbreaks requiring statistical analysis for preparedness.
- Analyzing small, localized historical outbreaks is crucial for understanding disease spread.
Purpose of the Study:
- To develop a spatial risk assessment framework for livestock disease incursions using small outbreak data.
- To infer disease dynamics and detection from incompletely observed outbreaks.
- To compare model selection methods for spatial transmission kernels.
Main Methods:
- Application of continuous time semi-Markov process models.
- Utilizing data-augmentation Markov Chain Monte Carlo within a Bayesian framework.
- Estimation of spatial transmission kernels and infection-to-detection times.
- Assessment of Deviance Information Criteria (DIC) and latent residual methods for model selection.
Main Results:
- Inference of disease dynamics is reliable for small outbreaks when the data-generating model is known.
- Risk assessments are highly dependent on the chosen transmission kernel.
- Latent residual methods reliably select the transmission kernel for small outbreaks.
- Latent residual methods show better correlation with predicted risk than DIC.
Conclusions:
- A robust framework for spatial risk assessment of livestock disease incursions is presented.
- Latent residual methods offer superior model selection for spatial transmission kernels compared to DIC, especially for small outbreaks.
- Accurate model selection is critical for reliable risk assessment in disease preparedness.
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