背景:新冠肺炎、气候变化的崛起凸显了这一点,和冲突,社会弱势群体对灾难的抵抗力最差。在传染病管理中,数学模型是一种常用的工具。研究人员应将社会脆弱性纳入模型,以增强其在反映现实世界动态方面的效用。我们进行了范围审查,以评估研究人员如何将社会脆弱性纳入传染病数学模型。
方法:该方法遵循JoannaBriggs研究所并更新了Arksey和O'Malley框架,由PRISMA-ScR检查表验证。PubMed,ClarivateWebofScience,Scopus,EBSCO非洲广泛信息,和Cochrane图书馆进行了系统搜索,以获取同行评审的已发表的文章。筛选和提取数据由两名独立研究人员完成。
结果:在4075个结果中,共有89篇文章。三分之二的文章使用了隔室模型(n=58,65.2%),四分之一使用基于代理的模型(n=24,27.0%)。总的来说,常规指标,即年龄和性别,是最常用的措施之一(n=42,12.3%;n=22,6.4%,分别)。只有一项指标与文化和社会行为有关(0.3%)。对于隔室模型,研究人员通常为社会脆弱性测量的每个级别构建不同的模型,并在模型方程中包括新参数或受影响的标准参数(n=30,51.7%)。对于所有基于代理的模型,将特征分配给宿主(n=24,100.0%),大多数模型包括年龄,接触行为,和/或性别(n=18,75.0%;n=14,53.3%;n=10,41.7%,分别)。
结论:鉴于公平有效的传染病管理的重要性,有潜力进一步扩大这一领域。我们的发现表明,没有从整体上考虑社会脆弱性。重点是纳入常规人口指标,但排除了影响健康结果的重要文化和社会行为。至关重要的是,开发将社会脆弱性作为前景的模型,不仅要设计更公平的干预措施,还要制定更有效的传染病控制和消除策略。此外,这项研究表明,数据源缺乏透明度,不一致的报告,缺乏与当地专家的合作,有限的研究侧重于文化指标的建模。这些挑战是未来研究的重点。
BACKGROUND: Highlighted by the rise of COVID-19, climate change, and conflict, socially vulnerable populations are least resilient to disaster. In infectious disease management, mathematical models are a commonly used tool. Researchers should include social vulnerability in models to strengthen their utility in reflecting real-world dynamics. We conducted a scoping
review to evaluate how researchers have incorporated social vulnerability into infectious disease mathematical models.
METHODS: The methodology followed the Joanna Briggs Institute and updated Arksey and O\'Malley frameworks, verified by the PRISMA-ScR checklist. PubMed, Clarivate Web of Science, Scopus, EBSCO Africa Wide Information, and Cochrane Library were systematically searched for peer-reviewed published articles. Screening and extracting data were done by two independent researchers.
RESULTS: Of 4075 results, 89 articles were identified. Two-thirds of articles used a compartmental model (n = 58, 65.2%), with a quarter using agent-based models (n = 24, 27.0%). Overall, routine indicators, namely age and sex, were among the most frequently used measures (n = 42, 12.3%; n = 22, 6.4%, respectively). Only one measure related to culture and social behaviour (0.3%). For compartmental models, researchers commonly constructed distinct models for each level of a social vulnerability measure and included new parameters or influenced standard parameters in model equations (n = 30, 51.7%). For all agent-based models, characteristics were assigned to hosts (n = 24, 100.0%), with most models including age, contact behaviour, and/or sex (n = 18, 75.0%; n = 14, 53.3%; n = 10, 41.7%, respectively).
CONCLUSIONS: Given the importance of equitable and effective infectious disease management, there is potential to further the field. Our findings demonstrate that social vulnerability is not considered holistically. There is a focus on incorporating routine demographic indicators but important cultural and social behaviours that impact health outcomes are excluded. It is crucial to develop models that foreground social vulnerability to not only design more equitable interventions, but also to develop more effective infectious disease control and elimination strategies. Furthermore, this study revealed the lack of transparency around data sources, inconsistent reporting, lack of collaboration with local experts, and limited studies focused on modelling cultural indicators. These challenges are priorities for future research.