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Optimal allocation of limited vaccine to control an infectious disease: Simple analytical conditions.
Rao, Isabelle J; Brandeau, Margaret L.
  • Rao IJ; Department of Management Science and Engineering, Stanford University, Stanford, CA, United States of America. Electronic address: isarao@stanford.edu.
  • Brandeau ML; Department of Management Science and Engineering, Stanford University, Stanford, CA, United States of America. Electronic address: brandeau@stanford.edu.
Math Biosci ; 337: 108621, 2021 07.
Article in English | MEDLINE | ID: covidwho-1207058
ABSTRACT
When allocating limited vaccines to control an infectious disease, policy makers frequently have goals relating to individual health benefits (e.g., reduced morbidity and mortality) as well as population-level health benefits (e.g., reduced transmission and possible disease eradication). We consider the optimal allocation of a limited supply of a preventive vaccine to control an infectious disease, and four different allocation

objectives:

minimize new infections, deaths, life years lost, or quality-adjusted life years (QALYs) lost due to death. We consider an SIR model with n interacting populations, and a single allocation of vaccine at time 0. We approximate the model dynamics to develop simple analytical conditions characterizing the optimal vaccine allocation for each objective. We instantiate the model for an epidemic similar to COVID-19 and consider n=2 population groups one group (individuals under age 65) with high transmission but low mortality and the other group (individuals age 65 or older) with low transmission but high mortality. We find that it is optimal to vaccinate younger individuals to minimize new infections, whereas it is optimal to vaccinate older individuals to minimize deaths, life years lost, or QALYs lost due to death. Numerical simulations show that the allocations resulting from our conditions match those found using much more computationally expensive algorithms such as exhaustive search. Sensitivity analysis on key parameters indicates that the optimal allocation is robust to changes in parameter values. The simple conditions we develop provide a useful means of informing vaccine allocation decisions for communicable diseases.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Viral Vaccines / Mass Vaccination / Epidemics / Models, Theoretical Type of study: Experimental Studies / Randomized controlled trials Topics: Vaccines Limits: Adult / Aged / Humans / Middle aged / Young adult Language: English Journal: Math Biosci Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Viral Vaccines / Mass Vaccination / Epidemics / Models, Theoretical Type of study: Experimental Studies / Randomized controlled trials Topics: Vaccines Limits: Adult / Aged / Humans / Middle aged / Young adult Language: English Journal: Math Biosci Year: 2021 Document Type: Article