tetano
Editor, Senior Moderator
Prev Vet Med
. 2026 Sep 24:257:107024.
doi: 10.1016/j.prevetmed.2026.107024. Online ahead of print.
Toshiya Arakawa 1
Affiliations
Highly pathogenic avian influenza (HPAI) outbreaks in domestic poultry require timely surveillance, but national-scale prediction is challenging because events are rare, spatially heterogeneous, and influenced by seasonal and environmental conditions. We developed a leakage-safe weekly risk-mapping framework for poultry HPAI outbreaks in Japan using open outbreak records, ERA5-Land reanalysis, and grid-level geographic and land-use data. The unit of analysis was a 10-km grid-week. For each target week, all 5491 national grid cells were ranked using only static, calendar, and preceding-week information, retrospectively emulating weekly spatial prioritization. Performance was evaluated using event-grid risk percentiles and top-k capture rates. The final panel comprised 1641,809 grid-week observations from 31 August 2020-18 May 2026. In the rolling fiscal-year evaluation, mean event-grid risk percentiles ranged from 0.695 to 0.713 across four predictor sets. In the event-week evaluation of 49 strict first-occurrence-like events, the baseline Extra Trees model using geography, seasonality, and previous-week temperature captured 22.4% and 42.9% of events within the top 10% and 20% of national grid cells, respectively. The external GIS and land-use Extra Trees model increased the corresponding capture rates to 38.8% and 59.2%; the top-10% difference was 16.3 %age points (paired bootstrap 95% CI, 2.0-30.6), although the exact test of discordant transitions was not significant (p = 0.0768). Performance therefore remained moderate despite improvement with GIS information. The framework should be interpreted as preliminary regional decision support rather than as a stand-alone outbreak warning system.
Keywords: Geographic information systems; Highly pathogenic avian influenza; Rare events; Spatial risk ranking; Surveillance prioritization.
. 2026 Sep 24:257:107024.
doi: 10.1016/j.prevetmed.2026.107024. Online ahead of print.
Leakage-safe weekly risk mapping of highly pathogenic avian influenza outbreaks in domestic poultry in Japan using open outbreak, meteorological, and geospatial data
Toshiya Arakawa 1
Affiliations
- PMID: 42800350
- DOI: 10.1016/j.prevetmed.2026.107024
Abstract
Highly pathogenic avian influenza (HPAI) outbreaks in domestic poultry require timely surveillance, but national-scale prediction is challenging because events are rare, spatially heterogeneous, and influenced by seasonal and environmental conditions. We developed a leakage-safe weekly risk-mapping framework for poultry HPAI outbreaks in Japan using open outbreak records, ERA5-Land reanalysis, and grid-level geographic and land-use data. The unit of analysis was a 10-km grid-week. For each target week, all 5491 national grid cells were ranked using only static, calendar, and preceding-week information, retrospectively emulating weekly spatial prioritization. Performance was evaluated using event-grid risk percentiles and top-k capture rates. The final panel comprised 1641,809 grid-week observations from 31 August 2020-18 May 2026. In the rolling fiscal-year evaluation, mean event-grid risk percentiles ranged from 0.695 to 0.713 across four predictor sets. In the event-week evaluation of 49 strict first-occurrence-like events, the baseline Extra Trees model using geography, seasonality, and previous-week temperature captured 22.4% and 42.9% of events within the top 10% and 20% of national grid cells, respectively. The external GIS and land-use Extra Trees model increased the corresponding capture rates to 38.8% and 59.2%; the top-10% difference was 16.3 %age points (paired bootstrap 95% CI, 2.0-30.6), although the exact test of discordant transitions was not significant (p = 0.0768). Performance therefore remained moderate despite improvement with GIS information. The framework should be interpreted as preliminary regional decision support rather than as a stand-alone outbreak warning system.
Keywords: Geographic information systems; Highly pathogenic avian influenza; Rare events; Spatial risk ranking; Surveillance prioritization.