Inferring Epidemiological Dynamics with Bayesian Coalescent Inference: The Merits of Deterministic and Stochastic Models

摘要: Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding infectious disease dynamics. Various models have been proposed infer details dynamics that describe epidemic progression. These include inference approaches derived Kingman’s coalescent theory. Here, we use recently described theory for develop stochastic deterministic susceptible–infected–removed (SIR) tree priors. We implement these in a Bayesian phylogenetic framework permit joint estimation SIR sample genealogy. assess performance two also juxtapose results obtained with published birth–death-sampling model inference. Comparisons are made by analyzing sets genealogies simulated under precisely known parameters. Additionally, analyze influenza A (H1N1) sampled Canterbury region New Zealand HIV-1 United Kingdom infection clusters. show both effective at estimating large fundamental reproductive number R0 size S0. Furthermore, find variant generally outperforms its counterpart terms error, bias, highest posterior density coverage, particularly smaller However, each is shown undesirable properties certain circumstances, especially outbreaks close one or small susceptible populations.