RAPID: Identifying the Drivers of Optimal COVID-19 Allocation

RAPID：确定最佳 COVID-19 分配的驱动因素

• 批准号: 2138192
• 负责人: Meagan Fitzpatrick
• 金额: $ 20万
• 依托单位: University of Maryland at Baltimore
• 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: 2022-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138192&HistoricalAwards=false
• 关键词: RAPID; Identifying; Drivers; Optimal; COVID

COVID-19 vaccines have been rapidly developed and deployed in many countries including the United States. Globally, supply remains constrained, especially in low-income countries. When supply is limited, vaccine allocation is often prioritized based on age, a policy decision in the United States that was supported by mathematical modeling. However, this allocation may not be ideal for low-income countries with different demographics and which may have substantially higher background immunity by the time vaccines become available. Furthermore, several variants of concern (VOC) have emerged with higher transmissibility, capable of immune evasion, or both. Such evolutionary shifts in traits of dominant or rising VOC may also impact optimal vaccine allocations. Similarly, if booster vaccines are required to prevent VOC in the US, optimal allocation may be affected by widespread partially-protective vaccine-induced immunity from the initial doses, compared to the largely unexposed populations for which the initial models were constructed. This research will identify the parameters which are most influential for determining the optimal vaccine allocation, as well as the interplay between these parameters. The project will have significant implications for informing policy globally for the COVID-19 pandemic. This project will also provide training opportunities for professional personnel. To execute this project, researchers will construct a dynamic transmission model of SARS-CoV-2, the causative agent of COVID-19, and integrate the model with an optimization algorithm that identifies the vaccine allocation strategy most effective at reducing disease burden given supply constraints. They will parameterize this model to a high-income country and a low-income country scenario, two settings with diverse demography, social contact patterns, and exposure histories. For both scenarios, the researchers will evaluate whether optimal allocation is robust to changes in parameters including background levels of natural or vaccine-induced immunity and vaccine performance against key VOC. The researchers will also conduct sensitivity analyses, including with regard to model design and geographic scale, as well as empirical uncertainty in parameter values. 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.

Covid-19-19疫苗已在包括美国在内的许多国家迅速开发和部署。在全球范围内，供应仍然受到限制，尤其是在低收入国家。当供应有限时，通常会根据年龄优先考虑疫苗分配，这是美国的一项政策决策，由数学建模支持。但是，这种分配可能不是人口统计学不同的低收入国家的理想选择，并且在疫苗可用时可能具有更高的背景免疫力。此外，出现了几种关注的变体（VOC），具有更高的传播性，能够免疫逃避或两者兼而有之。占主导地位或上升VOC的特征的这种进化转移也可能影响最佳疫苗分配。同样，如果需要加强疫苗来预防美国的VOC，则与在很大程度上未暴露的初始模型相比，最佳疫苗诱导的最初剂量的免疫受到了广泛的部分保护疫苗诱导的免疫力。这项研究将确定最有影响力的参数，这些参数是确定最佳疫苗分配以及这些参数之间的相互作用的参数。该项目将对联盟19日大流行的全球政策有重要意义。该项目还将为专业人员提供培训机会。为了执行该项目，研究人员将构建SARS-COV-2的动态传输模型，SARS-COV-2，即COVID-19的致病药物，并将模型与优化算法相结合，该算法识别疫苗分配策略在给定供应约束的情况下最有效地减轻疾病负担。他们将把这个模型参数化到一个高收入国家和低收入国家的情况，两个具有不同人口统计学的环境，社会接触模式和暴露历史。对于这两种情况，研究人员将评估最佳分配是否对参数的变化（包括自然或疫苗诱导的免疫力的背景水平以及针对关键VOC）的疫苗性能的变化。研究人员还将进行灵敏度分析，包括关于模型设计和地理规模，以及参数值的经验不确定性。该项目与CDC合作提供了资金，以支持快速响应研究项目，以进一步推进联邦传染病建模能力。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的影响来支持的支持标准，并被认为值得支持。 。

项目成果

Modelling the impact of a high-uptake bivalent booster scenario on the COVID-19 burden and healthcare costs in New York City.

• DOI: 10.1016/j.lana.2023.100555
• 发表时间: 2023-08
• 期刊: LANCET REGIONAL HEALTH-AMERICAS
• 作者: Pandey, Abhishek;Fitzpatrick, Meagan C.;Moghadas, Seyed M.;Vilches, Thomas N.;Ko, Charles;Vasan, Ashwin;Galvani, Alison P.
• 通讯作者: Galvani, Alison P.

Estimated US Pediatric Hospitalizations and School Absenteeism Associated With Accelerated COVID-19 Bivalent Booster Vaccination.

• DOI: 10.1001/jamanetworkopen.2023.13586
• 发表时间: 2023-05-01
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Fitzpatrick, Meagan C.;Moghadas, Seyed M.;Vilches, Thomas N.;Shah, Arnav;Pandey, Abhishek;Galvani, Alison P.
• 通讯作者: Galvani, Alison P.