Full Citation

Title: COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming

Citation Type: Journal Article

Publication Year: 2022

ISBN:

ISSN: 1932-6203

DOI: 10.1371/JOURNAL.PONE.0270524

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PMID:

Abstract: We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.

Url: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0270524

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Authors: Reddy Gujjula, Krishna; Gong, Jiangyue; Segundo, Brittany; Ntaimo, Lewis

Periodical (Full): PLOS ONE

Issue: 7

Volume: 17

Pages: e0270524

Data Collections: IPUMS USA

Topics: Health

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