RAPID: Identifying the Drivers of Optimal COVID-19 Allocation

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.