Data from: Infectious disease dynamics inferred from genetic data via sequential Monte Carlo

Genetic sequences from pathogens can provide information about infectious disease dynamics that may supplement or replace information from other epidemiological observations. Most currently available methods first estimate phylogenetic trees from sequence data, then estimate a transmission model conditional on these phylogenies. Outside limited classes of models, existing methods are unable to enforce logical consistency between the model of transmission and that underlying the phylogenetic reconstruction. Such conflicts in assumptions can lead to bias in the resulting inferences. Here, we develop a general, statistically efficient, plug-and-play method to jointly estimate both disease transmission and phylogeny using genetic data and, if desired, other epidemiological observations. This method explicitly connects the model of transmission and the model of phylogeny so as to avoid the aforementioned inconsistency. We demonstrate the feasibility of our approach through simulation and apply it to estimate stage-specific infectiousness in a subepidemic of HIV in Detroit, Michigan. In a supplement, we prove that our approach is a valid sequential Monte Carlo algorithm. While we focus on how these methods may be applied to population-level models of infectious disease, their scope is more general. These methods may be applied in other biological systems where one seeks to infer population dynamics from genetic sequences, and they may also find application for evolutionary models with phenotypic rather than genotypic data.病原体的遗传序列可提供传染病动力学相关信息，用以补充甚至替代其他流行病学观测所获取的信息。当前绝大多数可用方法均为先基于序列数据估算系统发育树（phylogenetic trees），再基于这些系统发育树条件化地构建传播模型。在有限的模型类别之外，现有方法无法保证传播模型与系统发育重建所依托的模型之间存在逻辑一致性，这类假设冲突会导致后续推断产生偏倚。本文提出一种通用、统计高效且即插即用（plug-and-play）的方法，可在利用遗传数据（若有需要亦可结合其他流行病学观测数据）的前提下，联合估算疾病传播与系统发育情况。该方法明确连接了传播模型与系统发育模型，从而规避了前述的假设不一致问题。我们通过模拟实验验证了该方法的可行性，并将其应用于美国密歇根州底特律市的一场HIV（人类免疫缺陷病毒）亚流行疫情，以估算不同阶段的传染性水平。在补充材料中，我们证明了该方法属于一种有效的序贯蒙特卡洛算法（sequential Monte Carlo algorithm）。尽管本文重点探讨此类方法在传染病种群水平模型中的应用场景，但其适用范围更为广泛：该方法可应用于其他需要从遗传序列中推断种群动态的生物系统，亦可用于基于表型数据而非基因型数据的进化模型研究。