Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America.
PLoS One. 2018 Nov 7;13(11):e0206926. doi: 10.1371/journal.pone.0206926. eCollection 2018.
Effective public health research and preparedness requires an accurate understanding of which virus species possess or are at risk of developing human transmissibility. Unfortunately, our ability to identify these viruses is limited by gaps in disease surveillance and an incomplete understanding of the process of viral adaptation. By fitting boosted regression trees to data on 224 human viruses and their associated traits, we developed a model that predicts the human transmission ability of zoonotic viruses with over 84% accuracy. This model identifies several viruses that may have an undocumented capacity for transmission between humans. Viral traits that predicted human transmissibility included infection of nonhuman primates, the absence of a lipid envelope, and detection in the human nervous system and respiratory tract. This predictive model can be used to prioritize high-risk viruses for future research and surveillance, and could inform an integrated early warning system for emerging infectious diseases.
有效的公共卫生研究和准备工作需要准确了解哪些病毒物种具有或有风险发展为人传人的能力。不幸的是，我们识别这些病毒的能力受到疾病监测的差距和对病毒适应过程的不完全理解的限制。通过将提升回归树拟合到 224 种人类病毒及其相关特征的数据上，我们开发了一种模型，该模型能够以超过 84%的准确率预测人畜共患病病毒的人类传播能力。该模型确定了几种可能具有未记录的人与人之间传播能力的病毒。预测人类传染性的病毒特征包括感染非人类灵长类动物、缺乏脂质包膜以及在人类神经系统和呼吸道中检测到。这种预测模型可用于为未来的研究和监测确定高风险病毒，并为新发传染病的综合早期预警系统提供信息。
