RAISE: IHBEM: Inclusion of Challenges from Social Isolation Governed by Human Behavior through Transformative Research in Epidemiological Modeling

RAISE：IHBEM：通过流行病学模型的变革性研究纳入人类行为所带来的社会孤立的挑战

• 批准号: 2230117
• 负责人: Folashade Agusto
• 金额: $ 100万
• 依托单位: University of Kansas Center for Research Inc
• 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=2230117&HistoricalAwards=false
• 关键词: RAISE; IHBEM; Inclusion; Challenges; Social

Project INSIGHT is a collaborative effort to develop novel and transformative research aimed at incorporating human social, behavioral, and economic interactions in mathematical epidemiological models. Project INSIGHT addresses two sets of questions about behavioral responses to social isolation during the COVID-19 pandemic: (1) How does compliance with isolation policies drive disease mitigation outcomes? and (2) Does social isolation lead to unanticipated negative social outcomes, and if so, how? Social isolation and individual distancing are key tools in mitigating large-scale infectious disease outbreaks. Yet, social interactions are crucial for the health and prosperity of individuals and their communities. Social isolation is associated with negative outcomes such as substance use and abuse, domestic violence, and reduced mental and physical health. These negative effects are often pronounced in rural, low-income, and older communities. The primary goal of INSIGHT is to model both positive and negative effects and thereby improve our understanding of the course of the COVID-19 pandemic and its long-term effect on society. 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) and the Division of Behavioral and Cognitive Sciences (BCS) in the Directorate of Social, Behavioral, and Economic Sciences (SBE).Project INSIGHT develops realistic epidemic models incorporating behavioral responses of compliance and adherence as follows: (1) Isolation compliance. Using a classical segmentation of populations into compliant and non-compliant groups, a switching function is defined to account for changes in behavior. Games with appropriate payoff functions that inform individuals’ behavioral choices are used. (2) Opioid misuse treatment adherence. A model according to severity of substance use disorder is created, implementing treatment adherence with game-derived utility functions and incorporating drug-seeking behavior of affected individuals, Model parameters like recovery and deaths are accounted for via well-defined behavioral functions. (3) Domestic violence. Focusing on intimate partners, economic-dependent functions are used to capture partners’ choices such as abuse, forgiveness, seeking help, or leaving the domestic violence cycle. The modeling efforts use data from several national and local sources. The outcome of Project INSIGHT modeling efforts is a synthetic, in-depth view of the balance of positive (reduction of disease transmission) and negative (substance abuse, domestic violence) implications of social isolation as a response to pandemic situations.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.

Project Insight是一项合作的努力，旨在开发旨在将人类社会，行为和经济互动纳入数学流行病学模型中的新颖和变革性研究。项目洞察力解决了COVID-19大流行期间对社会隔离的行为反应的两组问题：（1）遵守隔离政策如何驱动疾病缓解结果？ （2）社会隔离会导致意外的负面社会成果，如果是的话，如何？社会隔离和个人疏远是减轻大规模传染病暴发的关键工具。然而，社会互动对于个人及其社区的健康和繁荣至关重要。社会隔离与负面结果，例如使用和滥用，家庭暴力以及身心健康的降低有关。这些负面影响通常在农村，低收入和老年社区中发音。洞察力的主要目标是对积极和负面影响进行建模，从而提高我们对COVID-19大流行过程及其对社会的长期影响的理解。 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 Sc​​iences (SES) and the Division of Behavioral and Cognitive Sciences (BCS) in the Directorate of Social, Behavioral, and Economic Sc​​iences (SBE).Project INSIGHT develops realistic epidemic models encoding behavioral responses of compliance and adherence as以下内容：（1）隔离合规性。将种群的经典分割分解为合规和不合格的组，定义了一个切换函数以说明行为的变化。使用具有适当回报功能的游戏，可以使用信息，以告知个人行为选择。 （2）阿片类药物滥用治疗依从性。根据药物使用障碍的严重性，建立了一个模型，通过游戏衍生的实用程序功能实施治疗依从性，并结合了受影响个体的毒品寻求行为，恢复和死亡等模型参数通过明确定义的行为功能来解释。 （3）家庭暴力。专注于亲密伴侣，使用依赖经济的功能来捕获伴侣的选择，例如虐待，宽恕，寻求帮助或离开家庭暴力周期。建模工作使用来自几个国家和地方来源的数据。项目洞察力建模工作的结果是对积极（减少疾病传播）和负面（滥用疾病，家庭暴力）的平衡的合成，深入的看法，这是对大流行状况的回应。这奖反映了NSF的法规使命，并被认为是通过基金会的知识优点和广泛的评估来进行评估，这是值得通过评估的支持。

项目成果

MACHINE LEARNING FOR PREDICTING THE DYNAMICS OF INFECTIOUS DISEASES DURING TRAVEL THROUGH PHYSICS INFORMED NEURAL NETWORKS

机器学习通过物理信息神经网络预测旅行期间传染病的动态

• DOI: 10.1615/jmachlearnmodelcomput.2023047213
• 发表时间: 2023
• 期刊: Journal of Machine Learning for Modeling and Computing
• 作者: Ogueda-Oliva, Alonso G.;Martínez-Salinas, Erika Johanna;Arunachalam, Viswanathan;Seshaiyer, Padmanabhan
• 通讯作者: Seshaiyer, Padmanabhan

Literate programming for motivating and teaching neural network-based approaches to solve differential equations

用于激励和教授基于神经网络的方法来求解微分方程的文字编程

• DOI: 10.1080/0020739x.2023.2249901
• 发表时间: 2023
• 期刊: International Journal of Mathematical Education in Science and Technology
• 影响因子: 0.9
• 作者: Ogueda-Oliva, Alonso;Seshaiyer, Padmanabhan
• 通讯作者: Seshaiyer, Padmanabhan