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.

该奖项将支持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，经济学和社会如何相互塑造。传染病以复杂的方式改变着美国的经济、社会和文化，这些经济和社会变化影响着大流行病的发展方式。例如，持续的COVID-19大流行正在产生重大的经济、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，艾滋病毒流行病在1980年代对我们的社会产生了重大影响，远远超出了那些直接受到病毒影响的人。数学模型有助于理解这些复杂的相互作用。构建这种复杂的社会经济流行病学系统的模型需要各组成领域的专家共同努力。研究员预期研讨会所制订的研究议程，将有助研究界发掘新的数学方法和模型方法，以共同预测流行病学、经济和社会模式，从而以较低的社会和经济代价，减轻未来的流行病。传统的传染病模型方法是收集有关疾病的基本生命周期数据，并将其叠加到结构化人口数据上，以使用微分方程、网络、基于代理的模拟或其他相关建模方法来预测流行模式、健康结果。这些方法在一天前的预测中非常成功，但除了很大程度上的临时参数调整，这些标准方法几乎没有能力连接到基于行为的干预措施。这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（广义上的），而非药物干预措施在新病原体出现时至关重要。 在经济学和社会科学中，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一致联系。但学科的分歧很难弥合。在数据收集方面存在局限性，包括规模、精确度和代表性。过去几年来，学术文献的快速增长加剧了这一困难。汇集了不同的学者，科学家和建模人员将帮助每个对该领域感兴趣的人更好地了解到目前为止已经取得的成就，并为未来改进人类流行病学的数学模型制定研究议程。更多信息可以在会议网站www.example.com上找到http://the-mathepi-behavior-bridge.site/This项目由MPS数学科学部通过数学生物学计划和SBE社会与经济科学部社会学计划共同资助。该奖项反映了NSF的法定使命，并被认为值得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来支持。