Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth-death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth-death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.].

中文翻译：

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宏观进化和流行病学中组合证据分析的发生生死过程。

系统动力学模型通常旨在根据分子序列数据共同推断系统发育关系、模型参数，以及最近一段时间内的谱系数量。在流行病学和宏观进化领域，这些模型可用于分别估计过去感染个体的数量（流行率）或过去的物种数量（古多样性）。近年来，“全证据”分析得到了发展，该分析在统一的贝叶斯推理框架中结合了来自现存和过去抽样个体的分子和形态数据。即使是仅以采样时间为特征的采样个体，即缺乏我们称之为发生的形态和分子数据，也为估计过去的谱系数量提供了宝贵的信息。这里，我们围绕石化的生死过程提出了新的方法学发展，使我们能够 (i) 将发生数据纳入似然函数；(ii) 考虑分段恒定的出生率、死亡率和抽样率；(iii) 估计过去的世系数量，无论是否了解底层树。我们在 RevBayes 软件环境中实施我们的方法，使其能够与大量分子和形态进化模型一起使用，并在各种条件下使用模拟验证推理工作流程。最后，我们使用来自流行病学和宏观进化领域的两个经验数据集来说明我们的新实现。在流行病学方面，我们推断 2019 年冠状病毒病在钻石公主号船上爆发的流行率，通过共同考虑病例计数记录（发生）以及一小部分受感染个体的病毒序列。在宏观进化中，我们使用来自现存分类群的分子和形态学数据、来自化石的形态学数据以及大量化石事件来推断鲸类动物的多样性轨迹。事件和树木的联合建模有望进一步弥合传统流行病学和病原体基因组学以及古生物学和分子系统发育学之间的差距。【生死模型；流行病学；化石；宏观进化；事件；系统发育学；天际线。]。来自化石的形态数据，以及大量的化石发现。事件和树木的联合建模有望进一步弥合传统流行病学和病原体基因组学以及古生物学和分子系统发育学之间的差距。【生死模型；流行病学；化石；宏观进化；事件；系统发育学；天际线。]。来自化石的形态数据，以及大量的化石发现。事件和树木的联合建模有望进一步弥合传统流行病学和病原体基因组学以及古生物学和分子系统发育学之间的差距。【生死模型；流行病学；化石；宏观进化；事件；系统发育学；天际线。]。