EAGER: Epidemic Spread Modeling Using Hard Data

EAGER：使用硬数据进行流行病传播建模

• 批准号: 2130681
• 负责人: Evgenia Smirni
• 金额: $ 20.57万
• 依托单位: College of William and Mary
• 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: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2130681&HistoricalAwards=false
• 关键词: EAGER; Epidemic; Spread; Modeling; Using

Data-driven prediction models of the spread of COVID-19 are critical for guiding public health policy. Epidemiological models that use as input data in aggregated form can be used for prediction but the granularity of input can limit model usability. Models that are individual-centric are a lot more flexible but require as input the time series of every person's movement within a population: the exact location of each individual, the duration of the individual's stay at the location, and the transition to the next location. Due to privacy issues, accurate data of such granularity are not publicly available. The focus of this project is on the development of a prediction ecosystem that is individual-centric and can be used to foresee the spread of a highly contagious disease within a population that is active within an urban area. Such a model can be used to develop what-if scenarios to mitigate the spread of the disease and can become an indispensable tool for guiding policy decisions in future pandemics. This project will provide a flexible tool for epidemic modeling of COVID-19 and future pandemics. This project advocates the usage of agent-based models as an alternative to machine-learning for accurate prediction of the spread of contagious diseases. The aim is to create a prediction ecosystem for evaluating detailed scenarios: geographical restrictions of mobility, work from home orders/advisories, school closures (and partial openings under different conditions), points of interest operating under various capacities, time in quarantine, and vaccination priority, among others. The above scenarios can be modeled at various levels of detail with the aim to keep the model input small, compact, and flexible, but without compromising its prediction ability. Analysis of the above within the agent-based model setting identifies the most effective yet feasible input abstractions, similar to identifying the importance of feature selection in machine learning models. This tool, driven by anonymized cell-phone data will provide a robust modeling ecosystem that captures the effect of mitigation measures of contagious diseases using stochastic models that are complementary to machine-learning ones. Through this project, undergraduate and graduate students will be trained in the art of applied data science.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.

COVID-19 传播的数据驱动预测模型对于指导公共卫生政策至关重要。以聚合形式用作输入数据的流行病学模型可用于预测，但输入的粒度可能会限制模型的可用性。以个体为中心的模型更加灵活，但需要输入人口中每个人移动的时间序列：每个人的确切位置、个体在该位置停留的持续时间以及到下一个位置的过渡。由于隐私问题，如此粒度的准确数据无法公开。该项目的重点是开发一个以个人为中心的预测生态系统，可用于预测城市地区活跃人群中高度传染性疾病的传播。这样的模型可以用来制定假设情景，以减轻疾病的传播，并可以成为指导未来大流行的政策决策不可或缺的工具。该项目将为 COVID-19 和未来流行病的流行病建模提供灵活的工具。该项目提倡使用基于代理的模型作为机器学习的替代方案，以准确预测传染病的传播。目的是创建一个预测生态系统，用于评估详细场景：流动性的地理限制、在家工作订单/咨询、​​学校关闭（以及在不同条件下部分开放）、在不同容量下运行的兴趣点、隔离时间和疫苗接种优先权等。上述场景可以在不同的细节级别进行建模，目的是保持模型输入小、紧凑和灵活，但不影响其预测能力。在基于代理的模型设置中对上述内容的分析确定了最有效且可行的输入抽象，类似于确定机器学习模型中特征选择的重要性。该工具由匿名手机数据驱动，将提供一个强大的建模生态系统，使用与机器学习模型互补的随机模型来捕捉传染病缓解措施的效果。通过该项目，本科生和研究生将接受应用数据科学艺术的培训。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。

项目成果

Epidemic Spread Modeling for COVID-19 Using Cross-Fertilization of Mobility Data

使用流动性数据的交叉融合进行 COVID-19 流行病传播建模

• DOI: 10.1109/tbdata.2023.3248650
• 发表时间: 2023-10
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Schmedding, Anna;Pinciroli, Riccardo;Yang, Lishan;Smirni, Evgenia
• 通讯作者: Smirni, Evgenia

GeoSpread: an Epidemic Spread Modeling Tool for COVID-19 Using Mobility Data

GeoSpread：使用移动数据的 COVID-19 流行病传播建模工具

• DOI: 10.1145/3524458.3547257
• 发表时间: 2022-09
• 期刊: 2022
• 作者: Schmedding, Anna;Yang, Lishan;Pinciroli, Riccardo;Smirni, Evgenia
• 通讯作者: Smirni, Evgenia