Modeling the Coupled Dynamics of Influenza Transmission and Vaccination Behavior

流感传播和疫苗接种行为的耦合动力学建模

• 批准号: 9217563
• 负责人: Andrew Parker
• 金额: $ 41.66万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-05 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9217563
• 关键词: Adaptive Behaviors; Affect; Age; Antiviral Agents; Attitude; Awareness; Behavior; Behavioral; Behavioral Mechanisms; Behavioral Model; Belief; Benefits and Risks; Characteristics; Cities; Complex; Coupled; Cross-Sectional Studies; Custom; Data; Decision Making; Development; Disease; Effectiveness; Employment; Epidemic; Epidemiology; Exposure to; Foundations; Fright; Grant; Heterogeneity; Human; Incentives; Individual; Infection; Influenza; Influenza A Virus, H1N1 Subtype; Influenza vaccination; Longitudinal Surveys; Memory; Methodology; Methods; Modeling; Morbidity - disease rate; Nature; Outcome; Pattern; Perception; Physicians; Play; Policies; Population; Positioning Attribute; Predisposition; Property; Recommendation; Research; Risk; Role; Schools; Seasons; Severities; Shapes; Social Characteristics; Social Interaction; Social Network; Structure; Surveys; Time; United States; Vaccinated; Vaccination; Vaccines; Variant; Visit; Work; Workplace; base; behavior change; behavior influence; cost; design; experience; flu transmission; improved; influenza epidemic; influenza outbreak; innovation; interdisciplinary approach; interest; models and simulation; mortality; network models; pandemic disease; peer influence; programs; public health relevance; response; risk perception; seasonal influenza; social; transmission process; trend; uptake

 DESCRIPTION (provided by applicant): Decisions on whether or not to get vaccinated for seasonal influenza are largely motivated by attitudes and beliefs of the risks of infection and benefits of being vaccinated. The risk of influenza infection can change from season to season and depends on one's own vaccination status and the vaccination coverage among one's social net- work. Furthermore, attitudes and beliefs related to risk of infection can spread over a social network. Thus, in addition to personal attitudes, beliefs and experiences with vaccination and treatments for influenza, interactions of individuals on and characteristics of the social network can play important roles in shaping the nature and severity of influenza outbreaks and the effectiveness and cost of promoting vaccination. Our previous exploratory research has confirmed a strong dynamical interplay between behavior to get vaccinated, influenza epidemiology and social network structures. We collected nationally-representative cross- sectional survey data on behavioral factors associated with the decision to seek influenza vaccination. We then used these data to inform the development of an innovative agent-based model (ABM) that allowed experiences from past influenza seasons affect decisions to get vaccinated in the current season, and thus influence the course of an epidemic at the population level. In contrast to past and standard approaches, our models include two important properties of human decision-making: (a) memory and adaptability from past experiences and (b) peer-influences via rumor/information spreading. However, our ABM assumed a demographically homogenous population, considered just idealized social network structures and only considered a reduced set of attitudes and beliefs that affect the behavior to get vaccinated as suggested by our survey. In the proposed research we are interested in enhancing and refining our ABM by allowing our population to vary in terms of the demographic characteristics that influence vaccination, predisposition towards vaccination, and exposure to advice and opportunities for vaccination. We will conduct a four-year longitudinal panel study to construct an empirical behavioral model of decisions to get vaccinated for seasonal influenza that will include questions on additional attitudinal factors and considers a wider set of behavioral mechanisms. We will construct an improved social contact network structure that is representative of a large town/small city within the United States (US). We will consider different overlaying social contact network structures representative of different types of mixing. This approach will allow us to model social interactions and disease spread at a finer granularity and a higher level of realism than any existing random network model. We will calibrate our model in order to reproduce general yearly US trends of vaccination rates and infections by socio-demographic strata. We will then use our model to evaluate how behavioral and attitudinal factors influence the effectiveness of policies based on alternative vaccination promotion strategies and incentive-based strategies, and the expected changes when universal vaccines become available and awareness of the availability of antivirals increases.

 描述(由申请人提供)：关于是否接种季节性fl疫苗的决定在很大程度上是基于对感染风险和接种疫苗好处的态度和信念。感染infl流感的风险会随着季节的不同而变化，这取决于个人的疫苗接种状况和社交网络中的疫苗接种覆盖率。此外，与感染风险有关的态度和信念可以在社会上传播 网络。因此，除了个人在fluenza接种和治疗疫苗和治疗方面的态度、信念和经验外，个人在社交网络上的互动和特征 可在确定fl流感暴发的性质和严重程度以及促进疫苗接种的有效性和成本方面发挥重要作用。我们以前的探索性研究发现，接种疫苗的行为与fl流感流行病学和社会网络结构之间存在着强烈的动态相互作用。我们收集了具有全国代表性的横断面调查数据，内容是与寻求接种fl流感疫苗的决定相关的行为因素。然后，我们使用这些数据为一种创新的基于试剂的模型的开发提供了信息，该模型允许过去fl流感季节的经验影响在当前季节接种疫苗的决定，从而在fl人群水平上影响流行病的进程。与过去和标准的方法相比，我们的模型包括人类决策的两个重要属性：(A)来自过去经验的记忆和适应性，以及(B)通过谣言/信息传播的同行fl行为。然而，我们的ABM假设了人口统计上的同质性，只考虑了理想化的社会网络结构，并且只考虑了影响接种疫苗行为的一组减少的态度和信念，正如我们的调查所表明的那样。在拟议的研究中，我们有兴趣通过允许我们的人口在接种疫苗的人口特征、接种疫苗的易感性以及接受接种建议和接种机会的暴露方面的不同，来增强和恢复我们的fi。我们将进行一项为期四年的纵向小组研究，以构建fl季节性流感疫苗接种决策的经验行为模型，其中将包括有关其他态度因素的问题，并考虑更广泛的行为机制。我们将构建一个更好的社会联系网络结构，以代表美国境内的大城镇/小城市。我们会考虑不同的 叠加代表不同类型混合的社会联系网络结构。这种方法将允许我们以fiNER粒度和比任何现有的随机网络模型更高水平的现实性来对社会交互和疾病传播进行建模。我们将校准我们的模型，以再现美国疫苗接种率和社会人口阶层感染的一般年度趋势。然后，我们将使用我们的模型来评估fl中的行为和态度因素如何影响基于替代疫苗推广策略和基于激励的策略的政策的有效性，以及当通用疫苗可用和对抗病毒药物可用性的认识增加时预期的变化。