Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022).
Authors: Ding Jiafeng, Wang Yu, Liang Jinjiao, He Zhenhuan, Zhai Changhong, He Yinghao, Xu Jiayin, Lei Lei, Mu Jing, Zheng Min, Liu Boyang, Shi Mingxian
Journal: Frontiers in veterinary science
Summary
# Editorial Summary: Global Equine Influenza Risk Mapping (2005–2022) Researchers analysed 517 documented equine influenza (EI) outbreaks across 18 years using spatial epidemiology techniques to identify where and when the disease poses greatest risk to horse populations worldwide. Using SaTScan software, they identified 14 significant spatiotemporal clusters and developed a predictive model (achieving 92% accuracy) to map environmentally suitable areas for EI emergence. Three environmental factors emerged as critical drivers: ultraviolet radiation, equine population density, and precipitation during the coldest quarter—suggesting that EI risk is shaped by climate conditions that favour viral survival and transmission alongside horse concentration. High-risk zones currently cluster in Asia (particularly India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico), with projections indicating eastward expansion and geographical spread under most climate scenarios. For equine professionals managing biosecurity, vaccination programmes, and disease surveillance, these findings highlight which regions warrant enhanced vigilance and which environmental conditions should trigger heightened prevention protocols, whilst also informing long-term facility planning and international movement risk assessments.
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Practical Takeaways
- •Practitioners in high-risk regions (Asia and Americas) should implement enhanced biosecurity and vaccination protocols, particularly in areas with high horse density
- •EI surveillance should intensify during cold quarters in identified high-risk zones, as seasonal precipitation patterns influence disease occurrence
- •Geographic range and timing of EI risk is predicted to shift eastward in coming years; management strategies should be adapted regionally based on these forecasts
Key Findings
- •517 equine influenza occurrences from 2005-2022 identified 14 significant spatiotemporal clusters globally
- •Maxent predictive model achieved high accuracy (AUC = 0.920 ± 0.008) for identifying suitable EI occurrence areas
- •Annual average ultraviolet radiation, horse density, and precipitation of coldest quarter are the three most important environmental variables for EI occurrence
- •High-risk regions include Asia (India, Mongolia, China) and Americas (Brazil, Uruguay, USA, Mexico) with predicted eastward expansion and range changes under different climate scenarios