Impacts of Individual and Social Behavior on Influenza Dynamics and Control

描述(申请人提供)：流感传播及其引起的发病率和死亡率引起社会的极大关注。战略干预可能会极大地减少这些因素，但干预的有效性取决于公众的坚持，更广泛地说，取决于个人对实际和感知的健康风险做出的决定。该项目旨在确定最佳干预战略和政策，以显著提高对流行病和大流行性流感暴发的干预依从性。为了达到这一目标，我们将整合流行病学、数学建模、经济学、博弈论和实验心理学的知识和方法。接触模式往往是动态和高度可变的，从根本上影响疾病的传播。当个人决定接种疫苗、接受治疗、采取卫生预防措施或避免工作、学校或公共场所时，这些模式就会改变。我们将开发新的流行病学模型，明确考虑个人层面的感知和决定及其对流感传播潜在接触网络的影响。这些模型将捕捉流感的进化动态，包括抗原漂移和抗病毒耐药性的出现。我们将把博弈论方法应用到这些模型中，以评估不同的流感干预策略，包括疫苗接种、基于抗病毒的干预和非药物干预，并确定通过改变个人看法和决策的信息和激励计划来提高遵从性的战略机会。这些模型的接触模式和心理成分将基于对人口普查数据、工作流程和娱乐活动数据以及实时流感监测数据的贝叶斯分析，以及评估公众对疾病的了解和看法的调查研究。后者还将提供有关遵守行为、接触模式以及与流感相关的实时决策对这些模式的影响的信息。将现实的、感知驱动的个人层面决策纳入流行病学模型，将有助于评估干预措施和制定战略，以改进对流感疫情和大流行暴发的遵守情况。 公共卫生相关性：通过将现实的、感知驱动的个人层面决策纳入流行病学模型，该项目将改进预测干预成功的方法，并制定有效的战略，以提高干预依从性。

DESCRIPTION (provided by applicant): Influenza transmission and its resulting morbidity and mortality are of great concern to society. Strategic intervention may greatly reduce these factors, but the effectiveness of an intervention depends on public adherence, and, more generally, on individual decision making in response to actual and perceived health risks. This project aims to define optimal intervention strategies and policies that significantly improve intervention adherence for both epidemic and pandemic influenza outbreaks. To meet this objective, we will integrate knowledge and methods from epidemiology, mathematical modeling, economics, game theory, and experimental psychology. Contact patterns, which are often dynamic and highly variable, fundamentally influence the spread of disease. These patterns change as individuals make decisions to be vaccinated, accept treatment, take hygienic precautions, or avoid work, school, or public spaces. We will develop new epidemiological models that explicitly consider individual-level perceptions and decisions and their impacts on the contact networks underlying influenza transmission. These models will capture the evolutionary dynamics of influenza, including antigenic drift and the emergence of antiviral resistance. We will apply game-theoretical methods to these models to evaluate different influenza intervention strategies, including vaccination, antiviral-based interventions, and non-pharmaceutical interventions, and to identify strategic opportunities for improving adherence through informational and incentive programs that change individual perceptions and decisions. The contact patterns and psychological components of the models will be based on Bayesian analysis of census data, workflow and recreational mobility data, and real-time influenza surveillance data, as well as on survey studies that evaluate public knowledge and perceptions about the disease. The latter will also provide information on adherence behavior, contact patterns, and the impact of real-time influenza-related decisions on these patterns. Integrating realistic, perception-driven individual-level decision making into epidemiological models will facilitate the evaluation of interventions and the development of strategies to improve adherence both in epidemic and pandemic outbreaks of influenza. Public Health Relevance: By integrating realistic, perception-driven individual-level decision making into epidemiological models, this project will advance methods for predicting the success of interventions and for developing effective strategies for improving intervention adherence.