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，特别是在低收入国家。当供应有限时，疫苗分配通常根据年龄优先，这是美国的一项政策决定，得到了数学模型的支持。然而，这一分配对于人口结构不同的低收入国家可能并不理想，这些国家在疫苗上市时可能具有高得多的背景免疫力。此外，已经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。这种显性或上升VOC特征的进化变化也可能影响最佳疫苗分配。同样，如果美国需要加强疫苗来预防VOC，与构建初始模型的大部分未暴露人群相比，初始剂量的广泛部分保护性疫苗诱导免疫可能会影响最佳分配。这项研究将确定哪些参数是最有影响力的，以确定最佳的疫苗分配，以及这些参数之间的相互作用。该项目将对全球COVID-19疫情政策的制定产生重大影响。该项目还将为专业人员提供培训机会。为了执行这个项目，研究人员将构建一个SARS-CoV-2（COVID-19的病原体）的动态传播模型，并将该模型与一个优化算法相结合，该算法可以在给定供应限制的情况下确定最有效的疫苗分配策略，以减少疾病负担。他们将把这个模型参数化到一个高收入国家和一个低收入国家的情景中，这两个情景具有不同的人口统计学、社会接触模式和接触历史。对于这两种情况，研究人员将评估最佳分配是否对参数变化具有鲁棒性，包括天然或疫苗诱导的免疫力的背景水平以及疫苗对关键VOC的性能。研究人员还将进行敏感性分析，包括模型设计和地理尺度，以及参数值的经验不确定性。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

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.