RAPID: Identifying the Drivers of Optimal COVID-19 Allocation

包括美国在内的许多国家已经迅速开发和部署了COVID-19疫苗。在全球范围内，供应仍然有限，特别是在低收入国家。当供应有限时，通常根据年龄优先分配疫苗，这是美国的一项政策决定，得到了数学模型的支持。然而，对于人口结构不同的低收入国家来说，这种分配可能并不理想，这些国家在获得疫苗时可能具有高得多的背景免疫力。此外，一些变异的关注（VOC）出现了更高的传播率，能够免疫逃避，或两者兼而有之。挥发性有机化合物显性或上升特征的这种进化变化也可能影响最佳疫苗分配。同样，如果在美国需要加强疫苗来预防挥发性有机化合物，那么与构建初始模型的大部分未暴露人群相比，初始剂量广泛存在的部分保护性疫苗诱导免疫可能会影响最佳分配。本研究将确定对确定最佳疫苗分配最有影响的参数，以及这些参数之间的相互作用。该项目将对通报全球COVID-19大流行政策产生重大影响。该项目还将为专业人员提供培训机会。为实施该项目，研究人员将构建COVID-19病原体SARS-CoV-2的动态传播模型，并将该模型与优化算法相结合，确定在供应约束下最有效地减少疾病负担的疫苗分配策略。他们将把这一模型参数化为高收入国家和低收入国家的情景，这两种情况具有不同的人口结构、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稳健性，包括自然或疫苗诱导免疫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。该项目是与美国疾病控制与预防中心合作资助的，旨在支持快速反应研究项目，进一步提高联邦传染病建模能力。该奖项反映了美国国家科学基金会的法定使命，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。

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.