RAPID: Real-time Forecasting Models for Hospitalizations of Infectious Disease in the USA

RAPID：美国传染病住院实时预测模型

• 批准号: 2333435
• 负责人: Lauren Gardner
• 金额: $ 20万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333435&HistoricalAwards=false
• 关键词: RAPID; Real; time; Forecasting; Models

COVID-19 brought to light the extent to which infectious disease outbreaks can result in a significant burden on the healthcare system and societies in general. In order to support planning and decision-making efforts during periods of high disease transmission, expected disease burden and transmission patterns must be understood. This project will develop hospitalization forecasting models for the United States that exploit novel, high resolution publicly available data sets, namely waste water and genomic surveillance data, alongside more traditional epidemiological, mobility, demographic, socioeconomic, and behavioral data. These models will be designed to accurately assess the expected burden on local healthcare systems for cities in the United States, complementing the state and national level modeling frameworks that currently exist. A diverse group of students will lead the model development and dissemination of the results to the CDC through the COVID-19 Forecast Hub for COVID-19 and FluSight for Influenza, further expanding upon the established academic-government partnership. The publicly accessible submissions and ensemble forecast produced will serve to both enhance societies general understanding of infectious disease risk, and help improve science translation and literacy among the general public.The hospitalization forecasting models will utilize both mechanistic modeling and statistical data-driven approaches that combine disparate data inputs into meaningful predictive frameworks. This work will include the development of novel modeling techniques to further improve predictive capabilities. The high resolution, i.e., community and city-level, nature of the models will fill a gap in both the literature and practice, which to-date is dominate by state and national level forecasts. The highly local, more actionable spatial scales will both increase the utility of our models in practice, and provide a mechanism for local officials to distinguish harm across population groups, enabling more fair and equitable policy guidance for decision makers. The development of novel evaluation metrics that explicitly consider problem context will further increase the utility of our models, and offer a new set of performance tools to the broader modeling community. In the long term, our systems engineering approach to this research effort will contribute to the establishment of a robust, vetted set of tools that can be used for forecasting across a range of variables, during both seasonal cycles of respiratory viral disease and pandemic periods.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 揭示了传染病爆发可能给医疗保健系统和整个社会造成重大负担的程度。为了支持疾病高传播时期的规划和决策工作，必须了解预期的疾病负担和传播模式。该项目将为美国开发住院预测模型，利用新颖的高分辨率公开数据集，即废水和基因组监测数据，以及更传统的流行病学、流动性、人口、社会经济和行为数据。这些模型旨在准确评估美国城市当地医疗系统的预期负担，补充现有的州和国家级建模框架。多元化的学生群体将领导模型开发，并通过针对 COVID-19 的 COVID-19 预测中心和针对流感的 FluSight 向 CDC 传播结果，进一步扩大已建立的学术-政府合作伙伴关系。公开提交的意见和产生的整体预测将有助于增强社会对传染病风险的普遍理解，并有助于提高公众的科学翻译和素养。住院预测模型将利用机械建模和统计数据驱动的方法，将不同的数据输入结合到有意义的预测框架中。这项工作将包括开发新颖的建模技术，以进一步提高预测能力。模型的高分辨率（即社区和城市级别）性质将填补文献和实践中的空白，迄今为止，该空白由州和国家级别的预测主导。高度本地化、更具可操作性的空间尺度将增加我们模型在实践中的效用，并为地方官员提供区分不同人口群体的危害的机制，从而为决策者提供更公平和公正的政策指导。明确考虑问题背景的新颖评估指标的开发将进一步提高我们模型的实用性，并为更广泛的建模社区提供一套新的性能工具。从长远来看，我们针对这项研究工作的系统工程方法将有助于建立一套强大的、经过审查的工具，可用于在呼吸道病毒疾病的季节性周期和大流行期间预测一系列变量。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。