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: 2129023
• 负责人: Peter Raymond
• 金额: $ 2.93万
• 依托单位: Yale University
• 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=2129023&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.

该奖项将在5月6日至7日的2021年进行虚拟会议，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，使决策者能够了解感染性疾病，经济学和社会如何相互塑造。传染病以复杂的方式改变了美国的经济，社会和文化，这些经济和社会的变化影响了大流行的发展方式。例如，持续的共同199大流行正在具有实质性的经济，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，艾滋病毒的流行对1980年代的社会产生了重大影响，远远超出了立即受到该病毒影响的人。数学模型有助于理解这些复杂的相互作用。建造这种复杂的社会经济流行系统的模型需要来自组成领域的专家一起工作。 PI预测研讨会中制定的研究议程将促进研究社区发现新的数学方法和建模方法，以共同预测流行病学，经济和社会模式，以使未来的大流行学通过较低的社会和经济成本来减轻未来的大流行。模式，健康结果，使用微分方程，网络，基于代理的模拟或其他相关的建模方法。这些方法在日期预测中非常成功，但是除了在很大程度上进行参数调整外，这些标准方法几乎没有能力与基于行为的干预措施相连。这些意味着这些模型无助于衡量非药物干预措施的收益和成本（广泛定义），这在新颖的病原体出现时至关重要。在经济学和社会科学方面，有与流行病有关的专家知识和建模工作，可以用来更好地描述传播，同时提供与社会和经济的内部联系。但是纪律处分很难桥接。数据收集有限制，包括规模，精度和代表性。在过去的几年中，科学文献的快速增长使这一困难得到了扩大。汇集一群潜水员的学者，科学家和建模者将帮助每个对该领域感兴趣的人更好地了解到目前为止的成就，并制定研究议程，以改善未来人类流行病学中的数学模型。 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 Sc​​iences Sociology Program.This award reflects NSF's statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader impacts review 标准。