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

该项目旨在开发一个新的新冠肺炎传输深度学习预测平台，整合模型和数据不确定性下的多源信息。与流感、非典和MERS等其他病毒相比，新冠肺炎在许多方面都有所不同，包括对天气条件、病史以及公共卫生官员或普通公众应对措施的有效性的不确定性。一个重要的方面是将官方报告、大气数据和社交媒体数据等多源数据整合到新冠肺炎的业务性生物监测和实时预测中。拟议的生物监测框架将用于预测新冠肺炎的动态并加强缓解战略。此外，它还可以适用于追踪许多其他传染病，从而有助于我们整个社会的安全。此外，该项目将在数理生物学、统计学和深度学习内部和之间建立创新的联系，重点放在跨学科的研究生研究培训上。作为主要的预测框架，广泛使用的易感-暴露-感染-康复(SEIR)动态模型可以准确地描述疾病动力学，但需要精确的疾病参数知识，这可能需要很长时间才能准确估计。深度学习算法可能具有卓越的预测能力，但它们需要广泛的训练。这些事件的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监测、轶事和其他与健康有关的信息的多种来源。新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。其关键思想是将模拟SEIR模型视为深度学习模型的“代理”预训练器，导致重新训练预测模型以反映“真实世界”新冠肺炎进程所需的真实数据较少。然后，深度学习预测模型可以用于对未来的新冠肺炎动态进行预测，这可以与原始SEIR模型所做的预测进行比较。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，从而表示来自数据和最佳数学模型的强化学习。因此，新的预测框架将允许人们评估立即反应的影响，如宣布国家紧急状态、学校关闭或隔离，并可被视为迈向可解释的人工智能的一步，用于新冠肺炎生物保障。这项拨款是使用冠状病毒援助、救济和经济安全(CARE)法案提供的资金授予MPS的补充资金。这项奖励反映了国家科学基金会的法定使命，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。

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.