Evaluating transient risk factors for avian influenza outbreaks in Canada using case-crossover study and machine learning

Abstract

Highly Pathogenic Avian Influenza (HPAI) poses a severe biological and economic threat to the Canadian poultry industry. The transient and day-to-day meteorological and regional triggers remain poorly quantified due to spatial confounding in traditional epidemiological models. To isolate these triggers, this study utilizes a time-stratified case-crossover design, integrating a veterinary surveillance dataset of infected premises (2022–2024) with ERA5 meteorological reanalysis data. We employ the conditional difference method optimized with Ridge (L2) regularization. The national model demonstrated discriminative ability (ROC-AUC = 0.978; Brier score = 0.060). The analysis identified local proximity to an active outbreak as the strongest statistical predictor, with patterns consistent with wind-mediated transmission amplified by high wind speeds and relative humidity. A provincial-level stratification revealed that transmission mechanics are geographically distinct. In dense agricultural zones, extreme farm proximity dictates an effect that overpowers ambient weather factors. In dispersed geography of the Prairies, meteorological factors dominate infection triggers. These findings suggest that mitigation interventions must be tailored to regional factors.