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RAPID: Collaborative Research: Operational COVID-19 Forecasting with Multi-Source Information

RAPID：协作研究：利用多源信息进行可操作的 COVID-19 预测

基本信息

批准号：
2027793
负责人：
Yulia Gel
金额：
$ 8.02万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2021-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027793&HistoricalAwards=false
关键词：
RAPID Collaborative Research Operational COVID

项目摘要

This project aims to develop a new deep learning predictive platform for COVID-19 transmission, integrating multi-source information under model and data uncertainties. In contrast to other viruses such as influenza, SARS, and MERS, COVID-19 differs in a number of ways, including uncertainties in response to weather conditions, history of the disease, as well as the effectiveness of responses from public health officials or from the general public. An important aspect is to integrate multi-source data such as official reports, atmospheric variables, and social media data into operational biosurveillance and real-time prediction of COVID-19. The proposed biosurveillance framework will be used to forecast COVID-19 dynamics and to enhance mitigation strategies. In addition, it could also be applicable to tracking many other infectious diseases, thereby contributing to security of our society as a whole. Furthermore, the project will build innovative connections within and across mathematical biology, statistics, and deep learning, with a strong focus on interdisciplinary graduate research training.As the main forecasting framework, the widely used Susceptible-Exposed-Infected-Recovered (SEIR) dynamic models can accurately describe the disease dynamics, but only with precise knowledge of disease parameters, which can take a long time to accurately estimate. Deep learning algorithms can potentially have superior predictive ability, but they require extensive training. Another key challenge in the statistical modeling of these events is how to timely and systematically integrate multiple sources of surveillance, anecdotal, and other health-related information under uncertainty. The proposed new predictive approach is based on the interaction between multiple data sources, dynamical SEIR models, and deep learning algorithms. The key idea is to view simulation SEIR models as “surrogate” pre-trainers for the deep learning models, resulting in less real data needed to retrain the predictive model to reflect “real world” COVID-19 progression. Deep learning predictive models can then be used for making predictions about the future COVID-19 dynamics, which can be compared to the predictions made by the original SEIR model. Depending on which mathematical model makes better predictions, another model can be updated with the better prediction as inputs, thereby representing reinforcement learning from both data and the best mathematical model. As a result, the new predictive framework will allow one to assess impacts of the immediate responses such as declaration of a national emergency, a school closing, or a quarantine, and can be considered as a step toward interpretable AI for COVID-19 biosurveillance.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.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.

本项目旨在开发一种新型的深度学习预测平台，在模型和数据不确定性下整合多源信息。与流感、SARS和中东呼吸综合征等其他病毒相比，COVID-19在许多方面都有所不同，包括对天气条件、疾病史的反应的不确定性，以及公共卫生官员或公众反应的有效性。一个重要方面是将官方报告、大气变量和社交媒体数据等多源数据整合到COVID-19的业务生物监测和实时预测中。拟议的生物监测框架将用于预测COVID-19动态并加强缓解战略。此外，它还可用于跟踪许多其他传染病，从而为整个社会的安全作出贡献。此外，该项目将在数学生物学、统计学和深度学习领域建立创新联系，重点关注跨学科研究生研究培训。作为主要的预测框架，广泛使用的易感-暴露-感染-恢复（SEIR）动态模型可以准确地描述疾病动态，但需要精确的疾病参数知识，而准确估计需要很长时间。深度学习算法可能具有卓越的预测能力，但它们需要大量的训练。对这些事件进行统计建模的另一个关键挑战是如何在不确定的情况下及时和系统地整合多种来源的监测、轶事和其他与健康相关的信息。提出的预测方法基于多数据源、动态SEIR模型和深度学习算法之间的交互。关键思想是将模拟SEIR模型视为深度学习模型的“代理”预训练器，从而减少重新训练预测模型以反映“真实世界”COVID-19进展所需的真实数据。然后，可以使用深度学习预测模型对未来的COVID-19动态进行预测，并将其与原始SEIR模型的预测进行比较。根据哪个数学模型做出了更好的预测，可以用更好的预测作为输入来更新另一个模型，从而表示从数据和最佳数学模型中进行强化学习。因此，新的预测框架可以评估宣布国家紧急状态、关闭学校、隔离等紧急应对措施的影响，可以被认为是迈向可解释的人工智能生物监测的一步。这笔拨款是使用《冠状病毒援助、救济和经济安全（关怀）法案》分配给MPS的补充资金提供的资金。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。