Collaborative Research: Conference on Bridging Disciplinary Divides for Behaviorally Modulated Mathematical Models in Human Epidemiology

合作研究：弥合人类流行病学行为调节数学模型学科分歧会议

• 批准号: 2129157
• 负责人: Timothy Reluga
• 金额: $ 2.86万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129157&HistoricalAwards=false
• 关键词: Collaborative; Research; Conference; Bridging; Disciplinary

This award will support a virtual conference on May 6-7 2021 that will bring together some of the world’s leading mathematics, epidemiological, and social science scholars in order to chart a research agenda that enable policy makers to understand how infectious disease, economics, and society shape each other. Infectious diseases alter the United State's economy, society, and culture in complex ways, and these economic and social changes influence the way a pandemic develops. For example, the on-going COVID-19 pandemic is having substantial economic, educational, social, and societal consequences, including changes in work and housing habits, labor markets, and social justice dialogues. Similarly, the HIV epidemic had a major impact on our society in the 1980's, far beyond those immediately impacted by the virus. Mathematical models help make sense of these complex interactions. Constructing models of such complex socio-economic-epidemiological systems requires experts from the constituent fields to work together. The PIs anticipate the research agenda developed in the workshop will facilitate the research communities to discover new mathematical methods and modeling approaches to jointly forecast epidemiological, economic and social patterns so that future pandemics are mitigated with lower social and economic cost.The conventional approach to the modelling of infectious diseases is to collect basic life-cycle data on the disease, and to overlay that onto structured population data to predict prevalence patterns, health outcomes, using differential equations, networks, agent-based simulations, or another related modelling approaches. These approaches are quite successful at day-ahead prediction, but aside from largely ad hoc parametric adjustments, these standard approaches have almost no ability to connect to behavioral based interventions. These means that the models are not helpful for measuring the benefits and costs (broadly defined) of non-pharmaceutical interventions, which are critical when novel pathogens emerge. There is expert knowledge and modeling work, related to epidemics, in economics and social sciences that can be used to better describe transmission, while providing internally consistent connections to society and the economy. But the disciplinary divides are difficult to bridge. There are limits in data collection, including scale, precision, and representativeness. This difficulty has been amplified by the fast growth of the scholarly literature over the last few years. Bringing together a diverse group of scholars, scientists, and modelers will help everybody interested in the field better understand what has been accomplished so far and produce a research agenda for improving mathematical models in human epidemiology in the future. More information can be found at the conference web site http://the-mathepi-behavior-bridge.site/This project is jointly funded by the MPS Division of Mathematical Sciences through the Mathematical Biology Program, and the SBE Division of Social and Economic Sciences Sociology Program.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.

该奖项将支持于 2021 年 5 月 6 日至 7 日举行的虚拟会议，该会议将汇集一些世界领先的数学、流行病学和社会科学学者，以制定研究议程，使政策制定者能够了解传染病、经济学和社会科学如何影响人类健康。传染病以复杂的方式改变美国的经济、社会和文化，这些经济和社会变化影响着流行病的发展方式，例如，正在进行的 COVID-19 大流行对经济、社会和文化产生了重大影响。教育、社会和社会后果，包括工作和住房习惯、劳动力市场和社会正义对话的变化，同样，艾滋病毒流行对 20 世纪 80 年代的社会产生了重大影响，远远超出了该病毒的直接影响。模型有助于理解这些复杂的相互作用，构建此类复杂的社会经济流行病学系统的模型需要来自各组成领域的专家共同努力，预计研讨会上制定的研究议程将有助于研究界发现新的数学方法。和建模联合预测流行病学、经济和社会模式的方法，以便以较低的社会和经济成本减轻未来的流行病。传染病建模的传统方法是收集疾病的基本生命周期数据，并将其叠加到结构化数据上使用微分方程、网络、基于主体的模拟或其他相关建模方法，利用人口数据来预测患病率模式、健康结果。这些方法在日前预测方面非常成功，但除了主要的临时参数调整之外，这些标准方法。几乎没有能力这意味着这些模型无助于衡量非药物干预措施的效益和成本（广义），而当出现新的病原体时，与流行病相关的专家知识和建模工作至关重要。 ，在经济学和社会科学中可以用来更好地描述传播，同时提供与社会和经济内部一致的联系，但学科鸿沟难以弥合，包括数据收集的规模、精度和代表性。这个困难已经由于过去几年学术文献的快速增长，将不同的学者、科学家和建模者聚集在一起将有助于每个对该领域感兴趣的人更好地了解迄今为止所取得的成就，并制定改进数学的研究议程。更多信息请访问会议网站 http://the-mathepi-behavior-bridge.site/ 该项目由 MPS 数学科学部通过数学生物学计划共同资助，和该奖项反映了 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。