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

新型致病性冠状病毒，包括SARS-CoV和SARS-CoV-2，是由共同感染动物宿主的两种不同冠状病毒株基因重组而产生的。这些病毒在扩散到人类之前在宿主动物种群中传播。了解、监测和减轻重组和溢出需要确定对每种冠状病毒易感的宿主和对多种冠状病毒株易感的宿主（称为重组宿主）。然而，大多数冠状病毒与宿主的关联，以及因此产生的宿主和重组宿主，仍未得到确认。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，我们的目标是通过扩展我们久经考验的机器学习框架，包括鸟类宿主，来预测作为SARS-CoV-2宿主和重组宿主（WP1）的所有宿主物种。这将使我们能够在大流行期间监测SARS-CoV-2储存库，以及SARS-CoV-2可能重组产生新型致病性病毒的宿主。宿主物种的地理重叠是种间病毒共享的关键预测因子。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。这将使我们能够确定冠状病毒重组（WP2）的地理热点，从而能够开展针对性的空间监测和缓解工作。许多冠状病毒宿主与人类相互作用，要么通过地理/栖息地重叠等自然方式，要么被人类用作宠物/食物。利用来自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中确定的宿主的原位溢出的可能性。这将使决策者了解缓解溢出效应的物种和热点。

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.