Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models.

Autor:	Gonzalez-Parra G; Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, València, Spain.; Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, 87801, NM, USA., Mahmud MS; Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA., Kadelka C; Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Mar 07. Date of Electronic Publication: 2024 Mar 07.
DOI:	10.1101/2024.03.04.24303726
Abstrakt:	As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic. Competing Interests: Conflict of interest The authors declare there is no conflict of interest.
Databáze:	MEDLINE
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