Speaker
Description
Understanding viral transmission is important for responding to infectious diseases. For example, information about the geographic spread of a virus can inform policy decisions about transportation and borders. Estimates of the host species' movements in zoonotic viruses inform our knowledge of the nature and frequency of host jumps, and hence of zoonotic risk. Geographic and host movements are often estimated in phylogeography, in which evolutionary relationships between virus genome sequences are used to estimate the geographic locations or host species of unobserved ancestors and, hence, to reconstruct the movements of viruses in the past. However, phylogeography methods highly depend on differences in sampling among different locations or host species, which can bias location estimates. Here, we introduce a new phylogeography method, Sampling Aware Ancestral Inference (SAAI), which accounts for sampling differences and adjusts for them. We use simulations to show that with known sampling differences between different locations, SAAI infers past viral locations considerably more accurately than existing methods. We apply our method to the spread of the H5N1 virus in the United States in 2024, and explore how robust phylogeographic reconstructions are to differences in sampling between wild bird populations, cattle, humans and other species. We find that with the assumption of much (1000 times) lower sampling in wild birds than in cattle, the key spillover event from wild birds to cattle is estimated to occur later than with the assumption of only 5 times lower sampling in wild birds. Accounting for sampling changes inferences of past viral locations and the times of key viral movements.
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