RAPID: Statistical inference of incidence of SARS-CoV-2 in the US using multiple data streams to identify levels of immunity and the impact of non-pharmaceutical interventions

RAPID：使用多个数据流对美国 SARS-CoV-2 发病率进行统计推断，以确定免疫水平和非药物干预措施的影响

• 批准号: 2223843
• 负责人: Derek Cummings
• 金额: $ 20万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223843&HistoricalAwards=false
• 关键词: RAPID; Statistical; inference; incidence; SARS

The goal of this study is to integrate multiple, independent data sources to estimate the rate of SARS-CoV-2 infections across the US over time. Population-based SARS-CoV-2 serological assays are critical for understanding cumulative incidence and population-level immunity. The US CDC, in partnership with a number of laboratories, has conducted nationwide serosurveys which can help retrospectively assess the cumulative number of total infections. However, data from these surveys may be difficult to interpret due to heterogeneity in antibody response across individuals, by assay, and over time since infection. Reconciling patterns observed in seroprevalence with other data sources including reported COVID-19 cases and deaths can explain variation in seroprevalence across space and time in the US CDC. In addition, the project will estimate the proportion of the population with recent immunizing events (infection or vaccination) to understand the immunity landscape prior to the Omicron-variant-driven wave in 2021-2022 in the US. The project will develop tools to jointly analyze serology, caseand death data, and contribute to the training of a post-doctoral scholar.The primary objective in this study is to integrate multiple independent data streams using statistical and mechanistic models to estimate the rate of seroreversion in assays used in serosurveys across the US, and estimate seroprevalence and cumulative incidence over time by state. The model will provide information about SARS-CoV-2 transmission from case, hospitalization and death data by taking a multi-objective approach and adapting fast inference techniques that we have developed. Methods such as these have been applied to state-leveldata on COVID-19 incidence, including by this group. This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.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.

这项研究的目的是整合多个独立的数据来源，以估计美国各地SARS-CoV-2感染率随时间的变化。基于人群的SARS-CoV-2血清学检测对于了解累积发病率和人群水平的免疫力至关重要。美国疾病控制和预防中心与一些实验室合作，进行了全国性的血清调查，这有助于回顾性地评估累计感染总数。然而，由于个体之间、通过测定以及自感染以来随时间推移的抗体应答的异质性，这些调查的数据可能难以解释。在血清阳性率中观察到的模式与其他数据来源（包括报告的COVID-19病例和死亡）的一致性可以解释美国CDC中血清阳性率在空间和时间上的变化。此外，该项目还将估计最近发生免疫接种事件（感染或疫苗接种）的人口比例，以了解2021-2022年美国O型变异驱动浪潮之前的免疫状况。该项目将开发工具，共同分析血清学，caseand死亡数据，并有助于培养博士后scholar.The在这项研究中的主要目标是整合多个独立的数据流，使用统计和机械模型，以估计在美国各地的血清调查中使用的检测血清逆转率，并估计血清阳性率和累积发病率随着时间的推移，由国家。该模型将提供有关SARS-CoV-2传播的病例，住院和死亡数据，采取多目标的方法和适应快速推理技术，我们已经开发。诸如此类的方法已被应用于州一级的COVID-19发病率数据，包括该小组。该项目是与CDC合作资助的，以支持快速反应研究项目，进一步推进联邦传染病建模能力。该奖项反映了NSF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。

项目成果

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.

• DOI: 10.1038/s41467-023-37944-5
• 发表时间: 2023-04-19
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Garcia-Carreras, Bernardo;Hitchings, Matt D. T.;Johansson, Michael A.;Biggerstaff, Matthew;Slayton, Rachel B.;Healy, Jessica M.;Lessler, Justin;Quandelacy, Talia;Salje, Henrik;Huang, Angkana T.;Cummings, Derek A. T.
• 通讯作者: Cummings, Derek A. T.