EAGER: Epidemic Spread Modeling Using Hard Data

EAGER：使用硬数据进行流行病传播建模

基本信息

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

项目摘要

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的数据驱动的预测模型对于指导公共卫生政策至关重要。以汇总形式用作输入数据的流行病学模型可以用于预测，但是输入的粒度可以限制模型的可用性。以个人为中心的模型要灵活得多，但需要作为每个人在人群中移动的时间序列的输入：每个人的确切位置，个人在该位置的持续时间以及向下一个位置的过渡。由于隐私问题，这种粒度的准确数据无法公开可用。该项目的重点是以个人为中心的预测生态系统的发展，可以用来预见到高度传染性疾病在城市地区活跃的人群中的传播。这样的模型可用于开发如何减轻疾病传播的情况，并可能成为指导未来大流行中政策决策的必不可少的工具。该项目将为Covid-19和未来大流行病的流行建模提供灵活的工具。该项目主张使用基于代理的模型来替代机器学习，以准确预测传染性疾病的传播。目的是创建一个预测生态系统，以评估详细方案：地理限制出行，家庭订单/咨询工作，学校关闭（在不同条件下的部分开放），在各种能力下运行的兴趣点，Quartantine的时间和疫苗接种优先级。上述方案可以以各种详细级别进行建模，目的是保持模型输入小，紧凑且灵活，但不会损害其预测能力。基于代理的模型设置中以上的分析确定了最有效但可行的输入抽象，类似于确定机器学习模型中特征选择的重要性。由匿名手机数据驱动的该工具将提供一个可靠的建模生态系统，该生态系统使用与机器学习的随机模型捕获传染性疾病的缓解措施的影响。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的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持。