The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet , that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/ .

中文翻译：

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TNet：使用主机内应变多样性的传输网络推断及其在 COVID-19 传播的地理跟踪中的应用

疾病传播网络的推断是流行病学中的一个重要问题。建立传播网络的一种流行方法是使用从受感染宿主采样的疾病菌株的序列重建系统发育树，并根据该树推断传播。然而，大多数现有的用于传输网络推断的系统发育方法都是高度计算密集型的，并且不能考虑宿主内菌株的多样性。在这里，我们介绍了一种用于推断传输网络的新系统发育方法，TNet ，解决了这些限制。TNet 使用来自每个采样主机的多个应变序列来推断传输，并且比现有方法更简单、更准确。此外，TNet 具有高度可扩展性，能够区分模糊和明确的传输推断。我们在 560 个不同大小和不同主机、序列和传输特性的模拟传输网络以及 10 个具有已知传输历史的真实传输数据集上评估了 TNet。我们的结果表明，TNet 优于其他两种最近开发的方法，phyloscanner 和 SharpTNI，它们也考虑了宿主内菌株的多样性。我们还将 TNet 应用于从世界上许多国家的感染者中采集的大量 SARS-CoV-2 基因组，展示了我们的推理框架如何适用于准确推断地理传输网络。TNet 可从https://compbio.engr.uconn.edu/software/TNet/ .