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

作　　者：Gilberto Gonzalez-Parra Md Shahriar Mahmud Claus Kadelka 

机构地区：[1]Instituto de Matematica Multidisciplinar,Universitat Politecnica de Valencia,Valencia,Spain [2]Department of Mathematics,New Mexico Tech,801 Leroy Place,Socorro,87801,NM,USA [3]Department of Mathematics,Iowa State University,411 Morrill Rd,Ames,50011,IA,USA

出　　处：《Infectious Disease Modelling》2024年第4期1057-1080,共24页传染病建模（英文）

基　　金：funding from Maria Zambrano(UPV,Ministry of Universities of Spain by the European Union-Next generation EU);an Institutional Development Award(IDeA)from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM103451;partially supported by a travel grant(712537)from the Simons Foundation.

摘　　要：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.

关 键 词：Review Mathematical model Age COVID-19 Vaccine allocation Vaccine roll-out 

分 类 号：R563.1[医药卫生—呼吸系统] R186[医药卫生—内科学]