RAISE: IHBEM: Equilibrium, Network Formation, and Infectious-Disease Spread: Bridging the Divide between Mathematical Biology and Economics

RAISE：IHBEM：均衡、网络形成和传染病传播：弥合数学生物学和经济学之间的鸿沟

• 批准号: 2230074
• 负责人: Jason Xu
• 金额: $ 98.38万
• 依托单位: Duke 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: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230074&HistoricalAwards=false
• 关键词: RAISE; IHBEM; Equilibrium; Network; Formation

This project will bridge the gap between mathematical and statistical modeling of epidemic processes and economic models of human behavior. The COVID-19 pandemic has demonstrated the need for a new suite of mathematical models and corresponding inference tools to better understand the interdependence between the spread of infectious disease and changing human behaviors. Exciting progress has been made separately in mathematical biology and in economics. This project takes a convergent approach, incorporating how humans respond and make decisions as an epidemic unfolds, using key ideas and methodologies from both disciplines. This is critical to enable reliable forecasting and effective, real-time policy. This project is funded jointly 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).In this project, an economic (game-theoretic) model of individual decision-making is integrated into a continuous-time Markov chain framework for disease and contact dynamics. Under this approach, epidemic and contact network rates are affected by individually optimal agent behavior, which in turn depend on agents' (unobservable) social-economic costs. Likelihood-based inference methods to accommodate time-inhomogeneous rate parameters are derived, considerably extending the flexibility and realism of existing models, alongside rigorous algorithms for parameter estimation and uncertainty quantification. Bayesian data augmentation is used to account probabilistically for unobservable quantities as well as missing data via latent variables. Model selection using information-theoretic criteria simultaneously quantifies the improvement in fit reaped from behavioral models and guards against overfitting. Based on learned model parameters, proposed models are tested, possible outbreak scenarios are evaluated, and the impacts of alternative policies are compared via simulation studies and analyses of limiting behaviors.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.

该项目将弥合流行病过程的数学和统计模型与人类行为的经济模型之间的差距。 COVID-19 大流行表明需要一套新的数学模型和相应的推理工具，以更好地理解传染病传播与人类行为变化之间的相互依赖关系。数学生物学和经济学分别取得了令人兴奋的进展。 该项目采用融合方法，结合了人类在流行病发生时的反应和决策方式，并使用了两个学科的关键思想和方法。这对于实现可靠的预测和有效的实时政策至关重要。该项目由数学和物理科学局 (MPS) 下的数学科学部 (DMS) 和社会、行为和经济科学局 (SBE) 下的社会和经济科学部 (SES) 联合资助。在该项目中，个人决策的经济（博弈论）模型被集成到疾病和接触动态的连续时间马尔可夫链框架中。在这种方法下，流行病和接触网络率受到个体最佳代理行为的影响，而个体最佳行为又取决于代理的（不可观察的）社会经济成本。推导了基于似然的推理方法来适应时间不均匀的速率参数，大大扩展了现有模型的灵活性和现实性，以及用于参数估计和不确定性量化的严格算法。贝叶斯数据增强用于从概率上解释不可观测的数量以及通过潜在变量丢失的数据。使用信息论标准的模型选择同时量化了从行为模型中获得的拟合改进并防止过度拟合。基于学习到的模型参数，测试提出的模型，评估可能的爆发情景，并通过模拟研究和限制行为分析来比较替代政策的影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Fast Computation of Branching Process Transition Probabilities via ADMM

通过 ADMM 快速计算分支过程转移概率

• 发表时间: 2023
• 期刊: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics
• 作者: Awasthi, A;Xu, J.
• 通讯作者: Xu, J.

Distance-to-Set Priors and Constrained Bayesian Inference

距离设定先验和约束贝叶斯推理

• 发表时间: 2023
• 期刊: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics
• 作者: Presman, R;Xu, J.
• 通讯作者: Xu, J.

Equilibrium social activity during an epidemic

流行病期间的均衡社会活动

• DOI: 10.1016/j.jet.2022.105591
• 发表时间: 2023
• 期刊: Journal of Economic Theory
• 影响因子: 1.6
• 作者: McAdams, David;Song, Yangbo;Zou, Dihan
• 通讯作者: Zou, Dihan