Where coronaviruses hide, where novel strains are generated, and how they get to us: Predicting reservoirs, recombination, and geographical hotspots

新的致病冠状病毒，包括SARS-CoV和SARS-CoV-2，是由两个不同的冠状病毒株共同感染动物宿主而产生的。这些病毒在传播给人类之前在宿主动物种群中传播。理解、监测和缓解重组和溢出都需要识别对每种冠状病毒敏感的宿主和对多种冠状病毒株敏感的宿主(称为重组宿主)。然而，大多数冠状病毒-宿主之间的联系，因此宿主和重组宿主，仍然是未知的。这导致了对新型冠状病毒产生和溢出的潜在规模的低估。在这里，我们的目标是通过将我们经过测试的机器学习框架扩展到包括禽类宿主来预测所有作为SARS-CoV-2宿主和重组宿主(WP1)的宿主物种。这将使监测SARS-CoV-2大流行期间的宿主，以及SARS-CoV-2可能在其中重组产生新的致病病毒的宿主。宿主物种的地理重叠是物种间病毒共享的关键预测因素。通过构建物种层面的生态联系网络并将其与我们的框架相结合，我们将进一步完善我们的预测。这将使我们能够识别冠状病毒重组(WP2)的地理热点，从而能够进行特定的空间目标监测和缓解工作。许多冠状病毒宿主与人类相互作用，要么是通过自然的地理/栖息地重叠，要么是被人类用作宠物/食物。使用来自WP2的地理数据和来自开放获取来源的宿主物种利用数据，我们将(WP3)估计来自WP1中确定的宿主的就地溢出的可能性。这将向政策制定者通报物种和热点，以缓解溢出效应。

Novel pathogenic coronaviruses, including SARS-CoV and SARS-CoV-2, arise by genetic recombining of two different coronavirus strains co-infecting an animal host. These viruses circulate in reservoir animal populations before spillover to humans.Understanding, monitoring, and mitigating both recombination and spillover requires identifying hosts that are susceptible to each coronavirus and hosts susceptible to multiple coronavirus strains (termed recombination hosts).However, the majority of coronavirus-host associations, and therefore reservoirs and recombination hosts, remain unidentified. This has led to an underappreciation of the potential scale of novel coronavirus generation and spillover.Here, we aim to predict all host species which act as SARS-CoV-2 reservoirs and recombination hosts (WP1), by expanding our tried-and-tested machine-learning framework to include avian hosts. This will enable monitoring of SARS-CoV-2 reservoirs during the pandemic, and hosts in which SARS-CoV-2 could recombine to generate novel pathogenic viruses.Geographical overlap of host species is a key predictor of between-species viral sharing. By constructing a species-level ecological contact network and integrating it with our framework we will further refine our predictions. This will enable us to identify geographical hotspots of coronaviruses recombination (WP2), and therefore enable specific spatially-targeted surveillance and mitigation efforts.Many coronavirus hosts interact with humans, either naturally by e.g. geographic/habitat overlap, or are used by humans as e.g. pets/food. Using geographical data from WP2 and host species utilisation data from open-access sources, we will (WP3) estimate the in situ likelihood of spillover from hosts identified in WP1. This will inform policy makers of species and hotspots for spillover mitigation efforts.