RAISE: IHBEM: Integrating Traditional Survey and Digital Sociobehavioral Data into Infectious Disease Models for Long-Term Forecasting

RAISE：IHBEM：将传统调查和数字社会行为数据整合到传染病模型中进行长期预测

• 批准号: 2230125
• 负责人: Akihiro Nishi
• 金额: $ 99.77万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230125&HistoricalAwards=false
• 关键词: RAISE; IHBEM; Integrating; Traditional; Survey

The COVID-19 pandemic caused unprecedented impacts across all facets of everyday life. Alongside the ebb and flow of the disease, societal emotions, behaviors, and public health policies changed leading to a complex feedback loop: people’s behaviors shaped disease transmission, but as disease prevalence changed, so too did people’s behaviors. For example, a COVID-19 surge may trigger people’s anxiety and lead them to reduce their in-person interactions and put in place policies such as stay-at-home orders or mask mandates. Combined, these changes transiently control the surge until policies and behaviors relax and transmission rebounds again. Unlike many of the previous COVID-19 studies that have focused only on the unidirectional relationships within this feedback loop, such as the way policies impact behavior and the way pandemic trends impact policies, this project will develop models that can provide a holistic understanding of the complex interrelations between emotions, behavior, policies, and infection trends using the available COVID-19 data in the US and other countries. This effort will helpful for comprehensively understanding complex disease transmission dynamics, which will improve the abilities to anticipate infectious disease trends and enact meaningful public health policies for future infectious disease threats. In more concrete terms, the team will focus on two understudied socio-behavioral components that complement each other to form the feedback loop of COVID-19 dynamics. Thrust 1 will examine the role of psychological processing and decision-making using a collection of aggregated time-series data of SARS-CoV-2 infections, policies, emotions, and behaviors from 20 major metropolitan regions in the US (e.g., Reddit conversational data for tracking emotions). The goal will be to integrate psychological and behavioral processes mechanistically into COVID-19 epidemiological models and statistically estimate the time-varying relationships across the first two plus years of the pandemic. Thrust 2 will examine the role of dynamic and heterogeneous social contact patterns in infectious disease modeling in relation to social, behavioral, and political factors. Existing and newly obtained social mixing surveys that collect people’s contact data along with socio-behavioral variables will be analyzed to identify factors that characterize people’s contact patterns and responses to policies. Simulation studies will also be conducted to assess the degree to which incorporating these factors in infectious disease modeling could influence model predictions. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

COVID-19疫情对日常生活的各个方面造成了前所未有的影响。伴随着疾病的潮起潮落，社会情绪、行为和公共卫生政策发生了变化，从而形成了一个复杂的反馈循环：人们的行为塑造了疾病的传播，但随着疾病流行率的变化，人们的行为也发生了变化。例如，COVID-19疫情激增可能会引发人们的焦虑，导致他们减少面对面的互动，并制定诸如居家令或口罩规定等政策。结合起来，这些变化暂时控制激增，直到政策和行为放松，传输再次反弹。与之前的许多COVID-19研究不同，这些研究只关注这个反馈回路中的单向关系，例如政策影响行为的方式以及流行趋势影响政策的方式，该项目将开发模型，可以使用美国和其他国家的可用COVID-19数据来全面了解情绪，行为，政策和感染趋势之间的复杂相互关系。这一努力将有助于全面了解复杂的疾病传播动态，从而提高预测传染病趋势并针对未来传染病威胁制定有意义的公共卫生政策的能力。更具体地说，该团队将专注于两个研究不足的社会行为组成部分，它们相互补充，形成COVID-19动态的反馈回路。推力1将使用来自美国20个主要大都市地区的SARS-CoV-2感染，政策，情绪和行为的汇总时间序列数据（例如，Reddit用于跟踪情绪的会话数据）。其目标是将心理和行为过程机械地整合到COVID-19流行病学模型中，并对大流行最初两年多的时间变化关系进行统计估计。推力2将研究与社会，行为和政治因素有关的传染病建模中的动态和异构的社会接触模式的作用。将分析现有的和新获得的社会混合调查，这些调查收集人们的接触数据沿着社会行为变量，以确定人们接触模式和对政策反应的特征因素。还将进行模拟研究，以评估在传染病建模中纳入这些因素可能影响模型预测的程度。 该项目由数学和物理科学局（MPS）的数学科学部（DMS）和社会、行为和经济科学局（SBE）的社会和经济科学部（SES）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。