Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA.
Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA.
J Anim Ecol. 2022 Jun;91(6):1290-1302. doi: 10.1111/1365-2656.13694. Epub 2022 Apr 5.
Identifying reservoir host species is crucial for understanding the ecology of multi-host pathogens and predicting risks of pathogen spillover from wildlife to people. Predictive models are increasingly used for identifying ecological traits and prioritizing surveillance of likely zoonotic reservoirs, but these often employ different types of evidence for establishing host associations. Comparisons between models with different infection evidence are necessary to guide inferences about the trait profiles of likely hosts and identify which hosts and geographical regions are likely sources of spillover. Here, we use New World rodent-orthohantavirus associations to explore differences in the performance and predictions of models trained on two types of evidence for infection and onward transmission: RT-PCR and live virus isolation data, representing active infections versus host competence, respectively. Orthohantaviruses are primarily carried by muroid rodents and cause the diseases haemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS) in humans. We show that although boosted regression tree (BRT) models trained on RT-PCR and live virus isolation data both performed well and capture generally similar trait profiles, rodent phylogeny influenced previously collected RT-PCR data, and BRTs using virus isolation data displayed a narrower list of predicted reservoirs than those using RT-PCR data. BRT models trained on RT-PCR data identified 138 undiscovered hosts and virus isolation models identified 92 undiscovered hosts, with 27 undiscovered hosts identified by both models. Distributions of predicted hosts were concentrated in several different regions for each model, with large discrepancies between evidence types. As a form of validation, virus isolation models independently predicted several orthohantavirus-rodent host associations that had been previously identified through empirical research using RT-PCR. Our model predictions provide a priority list of species and locations for future orthohantavirus sampling. More broadly, these results demonstrate the value of multiple data types for predicting zoonotic pathogen hosts. These methods can be applied across a range of systems to improve our understanding of pathogen maintenance and increase efficiency of pathogen surveillance.
确定储存宿主物种对于理解多宿主病原体的生态学以及预测野生动物向人类传播病原体的风险至关重要。预测模型越来越多地用于识别生态特征，并优先监测可能的人畜共患病储存宿主，但这些模型通常使用不同类型的证据来建立宿主关联。比较具有不同感染证据的模型对于指导关于可能宿主的特征谱的推断以及识别哪些宿主和地理区域可能是溢出的来源是必要的。在这里，我们使用新世界啮齿动物-正粘病毒关联来探索基于两种感染证据（逆转录聚合酶链反应 (RT-PCR) 和活病毒分离数据）训练的模型的性能和预测之间的差异：分别代表活跃感染和宿主能力的 RT-PCR 和活病毒分离数据。正粘病毒主要由鼠科啮齿动物携带，并在人类中引起肾综合征出血热 (HFRS) 和汉坦病毒心肺综合征 (HCPS) 等疾病。我们表明，尽管基于 RT-PCR 和活病毒分离数据训练的增强回归树 (BRT) 模型都表现良好并捕获了一般相似的特征谱，但啮齿动物系统发育影响了先前收集的 RT-PCR 数据，并且使用病毒分离数据的 BRT 显示出比使用 RT-PCR 数据预测的储存宿主列表更窄。基于 RT-PCR 数据训练的 BRT 模型确定了 138 种未发现的宿主，而病毒分离模型确定了 92 种未发现的宿主，其中 27 种宿主被两种模型共同确定。预测宿主的分布集中在每个模型的几个不同区域，证据类型之间存在很大差异。作为一种验证形式，病毒分离模型独立预测了通过使用 RT-PCR 进行的先前的经验研究确定的几种正粘病毒-啮齿动物宿主关联。我们的模型预测为未来的正粘病毒采样提供了物种和地点的优先级列表。更广泛地说，这些结果证明了多种数据类型对于预测人畜共患病宿主的价值。这些方法可以应用于一系列系统，以提高我们对病原体维持的理解并提高病原体监测的效率。
