RAPID: Scenario Projections for Seasonal Influenza, SARS-CoV-2 and RSV Burden in the US (2023-2024)

RAPID：美国季节性流感、SARS-CoV-2 和 RSV 负担的情景预测（2023-2024 年）

• 批准号: 2345693
• 负责人: Lauren Meyers
• 金额: $ 19.51万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345693&HistoricalAwards=false
• 关键词: RAPID; Scenario; Projections; Seasonal; Influenza

To guide public health planning efforts for the 2023-2024 respiratory virus season, this study will use mathematical models to predict the individual and combined healthcare burden of COVID-19, influenza, and RSV in the United States. Since its emergence in 2019, COVID-19 has spread alongside influenza and respiratory syncytial virus (RSV) globally. While early pandemic interventions disrupted the seasonal transmission of influenza and RSV, the co-circulation of the three viruses strained healthcare resources in the United States during the 2022-2023 respiratory virus season. SARS-CoV-2 has yet to settle into a predictable seasonal cycle and the most recent RSV epidemic was significantly larger than pre-pandemic RSV seasonal waves. Public health and healthcare decision makers face considerable uncertainty in planning for future overlapping epidemics given that the timing and magnitude of seasonal virus epidemics are uncertain and that new medical countermeasures have recently become available for RSV that can significantly reduce disease severity for those at highest risk. The project will provide technical and translational training for two postdoctoral researchers and one Ph.D. student.The researchers will project the healthcare burden associated with COVID-19, influenza, and RSV in the United States throughout the 2023-2024 respiratory virus season under epidemiological scenarios that are defined in collaboration with officials at the US Centers for Disease Control and Prevention (CDC). Using a stochastic compartmental susceptible-exposed-infected-recovered (SEIR) modeling framework that was previously developed and validated for submitting projections to the CDC’s COVID-19 Scenario Modeling Hub and Influenza Scenario Modeling Hub, the projections will integrate estimates of population-wide immunity from derived from prior infections, vaccinations, and monoclonal antibody therapy for all three viruses and consider several scenarios for the future evolution of the viruses and the efficacy and uptake of medical countermeasures. The project will produce four rounds of projections between September 2023 and February 2024, which will be disseminated through rapid reports, peer-reviewed publications, and discussions with the CDC, Council of State and Territorial Epidemiologists, and the CDC’s Influenza and COVID-19 Scenario Modeling Hubs.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.

为了指导 2023-2024 年呼吸道病毒季节的公共卫生规划工作，本研究将使用数学模型来预测美国 COVID-19、流感和 RSV 的个体和综合医疗保健负担。自 2019 年出现以来，COVID-19 已与流感和呼吸道合胞病毒 (RSV) 一起在全球传播。虽然早期的大流行干预措施扰乱了流感和 RSV 的季节性传播，但这三种病毒的共同传播在 2022-2023 年呼吸道病毒季节给美国的医疗资源带来了压力。 SARS-CoV-2 尚未进入可预测的季节性周期，最近的 RSV 流行明显大于大流行前的 RSV 季节性波。鉴于季节性病毒流行的时间和程度不确定，而且最近针对 RSV 采取了新的医疗对策，可以显着降低高危人群的疾病严重程度，公共卫生和医疗保健决策者在规划未来重叠流行病时面临相当大的不确定性。该项目将为两名博士后研究人员和一名博士提供技术和转化培训。研究人员将在与美国疾病控制和预防中心 (CDC) 官员合作定义的流行病学情景下，预测 2023-2024 年呼吸道病毒季节期间美国与 COVID-19、流感和 RSV 相关的医疗保健负担。使用先前开发和验证的随机区室易感者-暴露-感染-恢复 (SEIR) 建模框架，用于向 CDC 的 COVID-19 情景建模中心和流感情景建模中心提交预测，这些预测将整合对所有三种病毒的先前感染、疫苗接种和单克隆抗体治疗得出的全人群免疫力的估计。 考虑病毒未来进化以及医疗对策的功效和采用的几种情景。该项目将在 2023 年 9 月至 2024 年 2 月之间产生四轮预测，这些预测将通过快速报告、同行评审出版物以及与 CDC、州和地区流行病学家委员会以及 CDC 的流感和 COVID-19 情景建模中心的讨论进行传播。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。 智力价值和更广泛的影响审查标准。