EAGER: Epidemic Spread Modeling Using Hard Data

数据驱动的新冠肺炎传播预测模型对于指导公共卫生政策至关重要。使用聚合形式的输入数据的流行病学模型可以用于预测，但输入的粒度可能会限制模型的可用性。以个人为中心的模型要灵活得多，但需要输入每个人在人群中移动的时间序列：每个人的确切位置，个人在该位置停留的时间，以及到下一个位置的过渡。由于隐私问题，这种粒度的准确数据无法公开获得。该项目的重点是开发一个以个人为中心的预测生态系统，可以用来预测一种高度传染性疾病在城市地区活跃的人群中的传播。这种模式可用于制定假设情景，以减缓疾病的传播，并可成为指导未来大流行中的政策决策的不可或缺的工具。该项目将为新冠肺炎疫情建模和未来疫情提供灵活的工具。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。其目的是创建一个预测生态系统，用于评估详细的情景：流动的地理限制、在家工作的订单/咨询、学校关闭(以及在不同条件下的部分开放)、在不同能力下运行的兴趣点、隔离时间和疫苗接种优先顺序等。上面的场景可以在不同的细节级别上建模，目的是保持模型输入的小、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但可行的输入抽象，类似于确定特征选择在机器学习模型中的重要性。这个由匿名手机数据驱动的工具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影响。通过这个项目，本科生和研究生将接受应用数据科学艺术方面的培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.