RAISE: IHBEM: Human Behavior Driven Mathematical Modeling and Forecasting of Respiratory Disease Transmission in Urban Settings

RAISE：IHBEM：人类行为驱动的数学建模和城市环境中呼吸道疾病传播的预测

• 批准号: 2229605
• 负责人: Sen Pei
• 金额: $ 99.87万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-15 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229605&HistoricalAwards=false
• 关键词: RAISE; IHBEM; Human; Behavior; Driven

Human behavior plays a central role in the transmission of respiratory pathogens such as SARS-CoV-2 and influenza; however, realistic representation of many behavioral processes is lacking in existing epidemiological models, which impedes accurate simulation and forecasting of disease spread. This project will use behavior theories and detailed data to develop behavior-driven epidemic models, study the transmission dynamics of COVID-19, and generate improved forecasting systems in urban settings. Studies will focus on New York City (NYC), a densely populated metropolitan area with large socioeconomic disparities that often experiences outbreaks earlier than surrounding regions. The proposed model will incorporate contact patterns indoors, where most transmission occurs, the adoption of protective measures such as mask-wearing, and reactive behavior change in response to infection risk. Research results will deepen understanding of behavior-disease interaction and produce next-generation predictive models for emerging respiratory diseases with validated forecasting accuracy. These efforts will fundamentally improve disease model realism by accurately incorporating behavior into mathematical models and improve the accuracy of respiratory disease forecasts. The developed forecasting systems can be deployed in real time to support epidemic control in the event of public health emergency. Research findings will be disseminated promptly to federal and local public health authorities leveraging ongoing collaborations to translate research into strategies for disease prevention and mitigation. The project will have long-term benefits for capacity building in pandemic preparedness and response.This project will be supported by rich and diverse datasets including neighborhood level COVID-19 data, detailed foot traffic records, mask-wearing survey data, socio-economic indicators, and behavioral characteristics collected from surveys in NYC neighborhoods. Proposed studies are organized around three synergistic research objectives: 1) use of core behavioral science theories – namely temporal discounting, loss aversion, agency, and norms and deviation – to quantify risk-driven behavior change; 2) incorporation of dwell time and crowdedness in different indoor settings and population-level masking into a metapopulation epidemic model at the neighborhood scale; 3) development of a predictive model for COVID-19 with behavior-disease feedbacks and systematic evaluation of its predictive skill through retrospective forecasts. The project will employ a breadth of interdisciplinary skills in mathematical modeling, statistical inference, behavior science, data science, and infectious disease epidemiology. These efforts will produce novel mathematical models incorporating human behaviors that enable improved operational forecasting of respiratory diseases.This project is jointly funded by the Division of Mathematical Science (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 project was also co-funded in collaboration with the CDC’s Center for Forecasting and Outbreak Analytics to support research projects to further advance federal infectious disease modeling, prevention and response capabilities.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.

人类行为在呼吸道病原体（如SARS-CoV-2和流感）的传播中起着核心作用;然而，现有的流行病学模型中缺乏对许多行为过程的真实表示，这阻碍了对疾病传播的准确模拟和预测。该项目将使用行为理论和详细数据开发行为驱动的流行病模型，研究COVID-19的传播动力学，并在城市环境中生成改进的预测系统。研究将侧重于纽约市，这是一个人口稠密的大都市地区，社会经济差异很大，往往比周边地区更早爆发疫情。拟议的模式将包括室内接触模式，其中大多数传播发生，采取防护措施，如戴口罩，以及应对感染风险的反应性行为变化。研究结果将加深对行为-疾病相互作用的理解，并为新出现的呼吸道疾病建立新一代预测模型，并验证预测准确性。这些努力将通过准确地将行为纳入数学模型，从根本上提高疾病模型的真实性，并提高呼吸系统疾病预测的准确性。所开发的预测系统可以在真实的时间内部署，以支持在发生公共卫生紧急情况时的流行病控制。研究结果将迅速传播到联邦和地方公共卫生当局，利用正在进行的合作，将研究转化为疾病预防和缓解战略。该项目将获得丰富多样的数据集支持，包括社区层面的COVID-19数据、详细的步行交通记录、戴口罩调查数据、社会经济指标以及从纽约社区调查中收集的行为特征。建议的研究是围绕三个协同研究目标组织的：1）使用核心行为科学理论-即时间折扣，损失厌恶，代理，规范和偏差-量化风险驱动的行为变化; 2）在不同的室内设置和人口水平掩蔽的停留时间和拥挤纳入一个集合种群流行病模型在邻里规模; 3）开发一个具有行为-疾病反馈的COVID-19预测模型，并通过回顾性预测对其预测技能进行系统评估。该项目将采用数学建模，统计推断，行为科学，数据科学和传染病流行病学的跨学科技能。这些努力将产生新的数学模型，结合人类的行为，使呼吸系统疾病的业务预测得到改善。该项目由数学和物理科学局（MPS）的数学科学处（DMS）和社会，行为和经济科学局（SBE）的社会和经济科学处（SES）共同资助。该项目还与CDC的预测和爆发分析中心合作共同资助，以支持进一步推进联邦传染病建模、预防和应对能力的研究项目。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。