Prioritizing COVID-19 vaccination. Part 2: Real-time comparison between single-dose and double-dose in Japan

Author:

Kobayashi Tetsuro,Nishiura Hiroshi

Abstract

<abstract> <p>Japan successfully implemented a mass vaccination program for coronavirus disease 2019 (COVID-19), immunizing more than 1 million persons a day by July 2021. Given the COVID-19 vaccination capacity limitations, an urgent question was raised regarding whether it would be better to (ⅰ) complete double-dose COVID-19 vaccination among healthcare personnel and older adults before beginning double-dose vaccination of younger adults (double-dose strategy) or (ⅱ) allocate a single dose of COVID-19 vaccine to all adults regardless of age before administering the second dose (single-dose-first strategy). We used an age-structured susceptible-infectious-recovered (SIR) compartment model to compare the effectiveness of possible COVID-19 vaccination strategies and the length of public health and social measures (PHSM) to minimize the cumulative COVID-19 disease risk and death toll. Our results indicate that if the single-dose-first strategy was taken, an estimated total of 1,387,078 persons, i.e., 263,315 children, 928,518 young adults, and 195,245 older adults, would develop COVID-19, resulting in 15,442 deaths. In contrast, if the double-dose strategy was taken instead, an estimated total of 1,900,172 persons, i.e., 377,107 children, 1,315,927 young adults, and 207,138 older adults, would develop COVID-19, yielding 17,423 deaths. Real-time investigation favored the disease transmission blocking option, i.e., single-dose vaccination strategy. Applying the single-dose-first strategy should yield a smaller epidemic size than applying the double-dose strategy; however, for both strategies, PHSM will be essential by the time second-dose COVID-19 vaccination is complete among all adults.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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