RAPID: Improved phylogenetic approaches for characterizing the epidemiological dynamics of COVID-19

从患者身上采集的病毒基因组构建的系统发育树包含了有关传染病传播和扩散的历史模式的信息。进化的数学模型使研究人员能够从系统发育树中编码的信息推断出关键的流行病学参数，例如传播率。由于未来的预测和政策决定取决于这些估计，因此充分了解其准确性和局限性至关重要。最近的研究结果表明，许多常用的疾病演变数学模型可能产生高度不准确的参数估计，并可能严重低估相关的不确定性，从而可能导致次优政策决策，这些决策要么无效，要么造成不必要的破坏。该项目将明确从系统发育树中可以可靠地推断出哪些流行病学见解，并将开发新的方法来准确表征COVID-19的时空传播。此外，该项目将根据系统发育数据确定影响COVID-19传播的环境、生物和政策因素。开发的计算方法将作为独立的开源R包共享，可用于分析和指导针对当前和未来流行病的决策。该项目将使用数学和计算方法来研究如何以及在多大程度上可以从系统发育数据估计传播、恢复和检出率以及基本繁殖比（R0）和病原体流行率。该项目将侧重于系统发育流行病学中常用的模型，包括出生-死亡抽样模型和随时间变化的发病率模型。该项目将研究这些模型的似然函数是否可以被引入特殊形式，以揭示哪些流行病学情景可能在统计上有所区别，类似于研究人员最近对宏观进化出生-死亡模型的研究。此外，研究人员将开发比目前可能的更强大的方法，从系统发育数据中表征病原体种群在空间和时间上的动态。为此，他们将模拟大量的流行病学模型，并寻找在统计上无法区分的情况下保存下来的数量。研究人员将重点研究SARS-CoV-2进化支（引起COVID-19的病毒），并将根据已发表的基因组数据研究他们的发现对COVID-19传播动态的影响。特别是，他们将对SARS-CoV-2亚分支之间以及空间和时间进行比较分析，以确定各种环境、生物和政策因素如何影响病毒的传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Phylogenetic trees constructed from viral genomes sampled from patients contain information about the historical pattern of transmission and dispersal of infectious diseases. Mathematical models of evolution allow researchers to infer critical epidemiological parameters, such as the transmission rate, from the information encoded in phylogenetic trees. Because future predictions and policy decisions depend on these estimates, it is crucial that their accuracy and limitations are well understood. Recent findings suggest that many commonly used mathematical models of disease evolution may yield highly inaccurate parameter estimates and may severely underestimate the associated uncertainty, thus potentially leading to sub-optimal policy decisions that are either ineffective or needlessly disruptive. This project will clarify precisely what epidemiological insights can be reliably inferred from phylogenetic trees and will develop new approaches to robustly characterize the spatial and temporal spread of COVID-19. Further, the project will determine which environmental, biological and policy factors affect the spread of COVID-19 based on phylogenetic data. The computational methods developed will be shared as standalone open source R packages that can be used for analyzing and guiding decisions against ongoing and future epidemics.This project will use mathematical and computational methods to examine how and to what extent transmission, recovery and detection rates as well as the basic reproduction ratio (R0) and pathogen prevalence could possibly be estimated from phylogenetic data. The project will focus on models commonly used in phylogenetic epidemiology, including birth-death-sampling models and coalescent models with variable rates through time. The project will investigate whether the likelihood function of these models can be brought into special forms that reveal which epidemiological scenarios can possibly be statistically distinguished, similarly to the researchers' recent work on macroevolutionary birth-death models. Further, the researchers will develop more robust measures to characterize pathogen population dynamics over space and time from phylogenetic data, than currently possible. To that end, they will simulate a large number of epidemiological models and search for quantities that are preserved across statistically indistinguishable scenarios. The researchers will focus on the SARS-CoV-2 clade (the virus causing COVID-19) and will examine the implications of their findings for COVID-19 spread dynamics, based on published genomic data. In particular, they will perform comparative analyses between SARS-CoV-2 sub-clades as well as over space and time, to determine how various environmental, biological and policy factors affect the spread of the virus.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.