MIVEGEC, Institut de Recherche pour le Développement, 34000, Montpellier, France.
Odum School of Ecology, University of Georgia, Athens, 30606, USA.
Ecohealth. 2022 Jun;19(2):246-258. doi: 10.1007/s10393-022-01599-3. Epub 2022 Jun 6.
Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.
许多牲畜疾病依赖野生动物来传播或维持病原体，野生动物-牲畜界面代表了新病原体在牲畜中出现的潜在地点。预测哪些病原体最有可能在未来出现，是传染病监测和情报的一个重要挑战。我们使用机器学习方法对奶牛、绵羊和猪的野生动物-牲畜界面的细菌关联进行了数据驱动的预测。我们的模型确定并对 76 到 189 种可能与每种牲畜相关联的潜在新型细菌进行了排名。已知和新型细菌的野生动物宿主在所有三种物种中都有共享，这表明针对这些宿主进行监测和/或控制工作可能会不成比例地降低向牲畜溢出的风险。通过预测野生动物-牲畜界面的病原体-宿主关联，我们展示了一种规划和预防牲畜疾病出现的方法。
