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

RAPID：改进的系统发育方法用于表征 COVID-19 的流行病学动态

• 批准号: 2028986
• 负责人: Stilianos Louca
• 金额: $ 10.53万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028986&HistoricalAwards=false
• 关键词: RAPID; Improved; phylogenetic; approaches; characterizing

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.

由患者采样的病毒基因组构成的系统发育树包含有关传播疾病传播和传播的历史模式的信息。进化的数学模型使研究人员可以从系统发育树中编码的信息中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策取决于这些估计，因此至关重要的是，它们的准确性和局限性得到充分理解。最近的发现表明，许多常用的疾病进化数学模型可能会产生高度不准确的参数估计，并可能严重低估了相关的不确定性，因此可能导致次优政策决策，而这些决策既无效或不必要地破坏性。该项目将准确阐明从系统发育树中可靠地推断出哪些流行病学见解，并将开发新的方法来鲁棒来表征Covid-19的空间和时间扩散。此外，该项目将根据系统发育数据来确定哪些环境，生物学和策略因素会影响Covid-19的传播。所开发的计算方法将作为独立开源R包共享，可用于分析和指导持续和未来的流行病的决策。该项目将使用数学和计算方法来检查如何以及如何以及在何种程度上以及何种程度的传输，恢复和检测率以及基本的再生率（R0）以及病原体的流行可能会从多个基本的繁殖率（R0）进行估算。该项目将重点关注系统发育流行病学中常用的模型，包括随时间变化速率的出生死亡采样模型和合并模型。该项目将调查这些模型的可能性功能是否可以将特殊形式带入特殊形式，这些形式可以揭示哪些流行病学情景可以在统计上进行区分，这与研究人员最近在宏观进化的出生死亡模型上的工作类似。此外，研究人员将制定更多的强大措施，以比当前可能的系统发育数据从空间和时间上表征病原体种群动态。为此，他们将模拟大量的流行病学模型，并搜索在统计上无法区分的场景中保存的数量。研究人员将重点关注SARS-COV-2进化枝（引起Covid-19的病毒），并将根据已发表的基因组数据研究其发现对Covid-19的含义。特别是，他们将在SARS-COV-2子层以及时间和时间之间进行比较分析，以确定各种环境，生物学和政策因素如何影响病毒的传播。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识绩效和更广泛影响的评估来通过评估来支持的，这是值得的。