Vaccine Allocation Priorities Using Disease Surveillance and Economic Data

Vaccination is a critical tool, along with suppression and treatment, for controlling epidemics such as SARS-CoV-2. To maximize the impact of vaccination, doses should be allocated to the highest value targets, accounting for health and potential economic benefits. We examine what allocation strategy is optimal and how to translate that strategy into actionable procurement decisions in the context of India. We compare 3 different allocation strategies (oldest first, highest contact rate first, random order) across 4 outcomes (lives saved, life-years saved, value of statistical lives saved, value of statistical life-years saved). We make 3 methodological contributions. First, we estimate the incremental health benefit of vaccination using novel, local seroprevalence data from India. Second, we estimate the value of statistical life-years using disaggregated, monthly data on consumption during the pandemic. Third, and most importantly, we estimate social demand curves for vaccines that can practically guide government procurement decisions. Our analysis yields 4 novel findings. First, the need to speed-up vaccination does not justify deviation from elderly-first prioritization. Second, much of the value of vaccination comes from improvements in consumption rather than longevity. Moreover, vaccination increases the value of a life year because it increases consumption. Third, social demand for vaccination falls over time as natural immunity from infections increases. Therefore, the slower a country vaccinates its population, the fewer doses it should procure. Fourth, there is enough variation in consumption and infection risk that it makes sense to vaccinate some areas before others. Our approach of connecting epidemiological models and data on health and consumption to economic valuation methods generalizes to other infection control strategies, such as suppression, and public health crises, such as influenza and HIV