Inference for a spatio-temporal model with partial spatial data: African horse sickness virus in Morocco

Area of Science:

• Veterinary Epidemiology
• Disease Modeling
• Arbovirology

Background:

• African horse sickness virus (AHSV) is a vector-borne pathogen transmitted by Culicoides midges.
• AHS disease affects equids and has a history of emergence in new regions, including Europe and Asia.
• Understanding AHSV transmission dynamics is crucial for effective disease control.

Purpose of the Study:

• To analyze a historical dataset of AHS emergence in Morocco (1989-1991) in a naive equid population.
• To estimate parameters of a spatial-temporal transmission model for AHSV.
• To assess the impact of spatial data specificity on transmission parameter estimation.

Main Methods:

• Utilized Sequential Monte Carlo and Markov Chain Monte Carlo techniques.
• Developed a spatial-temporal model incorporating a transmission kernel.
• Analyzed a unique historic dataset detailing infected equid premises in Morocco.

Main Results:

• Transmission parameters were estimated based on the distance between premises.
• Transmissibility estimations were consistent at village (1.3 km) and regional (99 km²) scales.
• Estimates varied significantly at the provincial scale (3000 km²), indicating scale-dependent effects.

Conclusions:

• The spatial specificity of data collection significantly influences AHSV transmission parameter estimates.
• Results provide data-driven insights for policymakers on optimal data collection during equine disease outbreaks.
• Findings can inform and improve AHS control strategies and surveillance efforts.