Department of Mathematics, University of Idaho, Moscow, Idaho, United States of America.
Department of Virology, Bernhard-Nocht Institute of Tropical Medicine, Hamburg, Germany.
PLoS Comput Biol. 2021 Mar 3;17(3):e1008811. doi: 10.1371/journal.pcbi.1008811. eCollection 2021 Mar.
Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.
预测来自野生动物或家畜的储存宿主种群的病原体溢出风险对于有效实施干预措施至关重要，例如野生动物接种疫苗或扑杀。由于溢出事件的偶发性和数据的有限可用性，开发和验证稳健、具有空间明确性的预测具有挑战性。最近，人们开始通过利用机器学习方法在这方面取得进展。然而，现有方法的一个重要弱点是，它们通常在训练过程中结合人类和储存宿主的感染数据，从而将病原体在储存宿主种群中的流行率所带来的风险与归因于实际溢出到人类种群中的风险相混淆。因为有效的干预措施规划需要将这些风险因素分开，所以我们开发了一个多层机器学习框架来分离这些过程。我们的方法首先训练模型来预测主要储存宿主的地理范围以及该范围内发生病原体的子集。然后，将这些针对储存宿主的模型的乘积预测的溢出风险拟合到关于历史上人类种群中实际溢出模式的数据。结果是一个具有特定地理位置的溢出风险预测，可以轻松分解并用于指导有效的干预。将我们的方法应用于拉沙病毒，这是一种经常在西非溢出到人类种群中的人畜共患病病原体，得到的模型解释了历史溢出模式中地理变异的一个适度但具有统计学意义的部分。当与感染动力学的机械数学模型结合使用时，我们的溢出风险模型预测每年有 897,700 人在整个西非感染拉沙病毒，其中尼日利亚占这些人类感染的一半以上。
