IHBEM: Using socioeconomic, behavioral and environmental data to understand disease dynamics: exploring COVID-19 outcomes in Oklahoma

IHBEM：利用社会经济、行为和环境数据了解疾病动态：探索俄克拉荷马州的 COVID-19 结果

• 批准号: 2327844
• 负责人: Patrick Stephens
• 金额: $ 50万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327844&HistoricalAwards=false
• 关键词: IHBEM; Using; socioeconomic; behavioral; environmental; IHBEM; Using; socioeconomic; behavioral; environmental

One of the most critical modern challenges is to better understand the where, why and how oflarge disease outbreak occurrence. Research shows that the frequency of large disease outbreaks is increasing over time globally, and yet differences in outcomes remain poorly understood. This research will explore the factors that drove variation in COVID-19 outcomes across the counties and metropolitan areas of Oklahoma, particularly which areas had more or fewer cases than would be expected based on their overall population size. The investigators will look at both environmental factors, such as weather patterns and air quality, and socioeconomic factors such as numbers of doctors and differences in the proportion of individuals that were willing to be vaccinated. The investigators will also conduct surveys of individual across the state to try and better understand why people made the healthcare choices that they did and how behavior drove differences in outcomes. Understanding all of these factors requires a team with diverse expertise. Traditionally, most mathematical and quantitative models for disease dynamics have been developed and studied by mathematicians, ecologists, and computer scientists. However, understanding differences in attitudes towards health care measures and how they originate is more the purview of social scientists and historians. By building a team of collaborators spanning all of these disciplines, the research team will be able to build a more complete picture of COVID-19 outcomes in Oklahoma. This will in turn suggest what actions may be most effective to try and best mitigate the effects of both COVID and other large-scale disease events in the future. The final product of this work will include a new data repository and a public-facing intelligent epidemiological modeling platform powered by Jupyter Notebooks. The project will also provide outreach and training, including to students from underrepresented groups.Increases in outbreak frequency seem to be related to globalization and other human activities. Yet the effects of most human behavioral, social and economic factors on outbreak risk are rarely quantified. Relevant social factors can be hard to measure, often needing specialists to generate and interpret data. However social scientists with expertise to do so are rarely trained in mathematical modelling of disease dynamics. To address these challenges, the investigators will focus on developing data sources and mathematical models that can be used to explore COVID-19 outcomes in Oklahoma. The project will be a true collaboration between social scientists and experts in modelling infectious diseases. Oklahoma is understudied, and is spatially heterogeneous such that models of disease dynamics in Oklahoma are likely to be generalizable to many other regions of the US. The Investigators will generate protocols for standardizing existing data on behavioral and socioeconomic factors as well as develop new data sources. The team will develop statistical models of past outbreaks, and mathematical models reflecting factors shown to have driven COVID-19 dynamics empirically. The latter work will demonstrate how baseline SIR-like models can be modified to reflect human behavioral factors. The Investigators will also contrast the performance of models based on existing data on socioeconomic factors with models incorporating new survey data on variation in behaviors and attitudes related to primary and secondary prevention. The code and datasets to be generated will be made freely available and searchable in an intelligent epidemiological modeling framework, which will enable other researchers to easily iterate on them.This project is jointly funded 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).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.

最关键的现代挑战之一是更好地了解疾病爆发的地点，原因和方式。研究表明，随着时间的流逝，大型疾病暴发的频率在全球范围内增加，但结局的差异仍然很少。 这项研究将探讨俄克拉荷马州县和大都市地区共同成果差异的因素，尤其是该地区的案例比基于整体人口的整体规模要比预期的要少。 调查人员将研究两个环境因素，例如天气模式和空气质量，以及社会经济因素，例如医生数量和愿意接种疫苗的个人比例的差异。 调查人员还将在全州进行个人调查，以尝试更好地理解人们为何做出医疗保健选择以及行为如何推动结果的差异。了解所有这些因素都需要一个具有多种专业知识的团队。传统上，数学家，生态学家和计算机科学家已经开发和研究了大多数用于疾病动态的数学和定量模型。但是，了解对医疗保健措施的态度及其产生的态度的差异更多是社会科学家和历史学家的权限。通过建立一个跨越所有这些学科的合作者团队，研究团队将能够对俄克拉荷马州的Covid-19结果进行更完整的了解。反过来，这将表明哪些行动可能最有效地尝试减轻未来Covid和其他大规模疾病事件的影响。这项工作的最终产品将包括一个新的数据存储库和由Jupyter Notebook提供支持的公共智能流行病学建模平台。该项目还将提供宣传和培训，包括来自代表性不足的群体的学生。爆发频率中的信息似乎与全球化和其他人类活动有关。然而，很少量化大多数人类行为，社会和经济因素对爆发风险的影响。 相关的社会因素可能很难衡量，通常需要专家来生成和解释数据。 但是，具有专业知识的社会科学家很少在疾病动态的数学建模中接受培训。 为了应对这些挑战，研究人员将专注于开发可用于探索俄克拉荷马州共同结果的数据源和数学模型。 该项目将是社会科学家与建模传染病的专家之间的真正合作。 俄克拉荷马州已经研究了，并且在空间上是异质的，因此俄克拉荷马州的疾病动态模型可能可以推广到美国许多其他地区。研究人员将生成协议，以标准化有关行为和社会经济因素的现有数据，并开发新的数据源。该团队将开发过去爆发的统计模型，数学模型反映了证明已通过经验驱动Covid-19动力学的因素。 后者的工作将证明如何修改基线SIR样模型以反映人类的行为因素。研究人员还将基于现有的社会经济因素数据与模型的模型与模型进行对比，并结合了与主要和次要预防有关的行为和态度变化的新调查数据。 要生成的代码和数据集将在智能的流行病学建模框架中免费获得和搜索，该框架将使其他研究人员易于迭代它们。该项目由数学科学（DMS）在数学科学和物理科学（MPS）以及经济和经济科学和经济科学局（MPS）的行为和经济阶层（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSS）（SSSSS）（SSSSSS）（SSSSSS）（SSSS）（SSSSS）（sess）（SSSSS）ins SES（SSSSS）incoral（SSSSS）中，该公司共同资助。 （SBE）。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响审查标准的评估来支持的。