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Optimizing Vaccine Allocation to Combat the COVID-19 Pandemic
Dimitris Bertsimas; Joshua Kiefer Ivanhoe; Alexandre Jacquillat; Michael Lingzhi Li; Alessandro Previero; Omar Skali Lami; Hamza Tazi Bouardi.
Afiliação
  • Dimitris Bertsimas; Massachusetts Institute of Technology
  • Joshua Kiefer Ivanhoe; Massachusetts Institute of Technology
  • Alexandre Jacquillat; Massachusetts Institute of Technology
  • Michael Lingzhi Li; Massachusetts Institute of Technology
  • Alessandro Previero; Massachusetts Institute of Technology
  • Omar Skali Lami; Massachusetts Institute of Technology
  • Hamza Tazi Bouardi; Massachusetts Institute of Technology
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20233213
ABSTRACT
The outbreak of COVID-19 has spurred extensive research worldwide to develop a vaccine. However, when a vaccine becomes available, limited production and distribution capabilities will likely lead to another challenge who to prioritize for vaccination to mitigate the near-end impact of the pandemic? To tackle that question, this paper first expands a state-of-the-art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across subpopulations. It then integrates this predictive model into a prescriptive model to optimize vaccine allocation, formulated as a bilinear, non-convex optimization model. To solve it, this paper proposes a coordinate descent algorithm that iterates between optimizing vaccine allocations and simulating the dynamics of the pandemic. We implement the model and algorithm using real-world data in the United States. All else equal, the optimized vaccine allocation prioritizes states with a large number of projected cases and sub-populations facing higher risks (e.g., older ones). Ultimately, the optimized vaccine allocation can reduce the death toll of the pandemic by an estimated 10-25%, or 10,000-20,000 deaths over a three-month period in the United States alone. Highlights- This paper formulates an optimization model for vaccine allocation in response to the COVID-19 pandemic. This model, referred to as DELPHI-V-OPT, integrates a predictive epidemiological model into a prescriptive model to support the allocation of vaccines across geographic regions (e.g., US states) and across risk classes (e.g., age groups). - This paper develops a scalable coordinate descent algorithm to solve the DELPHI-V-OPT model. The proposed algorithm converges effectively and in short computational times. Therefore, the proposed approach can be implemented efficiently, and allows extensive sensitivity analyses for scenario planning and policy analysis. - Computational results demonstrate that optimized vaccine allocation strategies can curb the death toll of the COVID-19 pandemic by an estimated at 10-25%, or 10,000-20,000 deaths over a three-month period in the United States alone. These results highlight the critical role of vaccine allocation to combat the COVID-19 pandemic, in addition to vaccine design and vaccine production.
Licença
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo prognóstico / Rct Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo prognóstico / Rct Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
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