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揭示了传染病暴发可能导致医疗保健系统和整个社会的重担。为了支持高疾病传播期间的计划和决策工作，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，以利用新颖的，高分辨率的公开数据集，即浪费水和基因组监测数据，以及更传统的流行病学，流动性，人口统计学，社会经济和行为数据。这些模型将旨在准确评估美国城市的当地医疗保健系统的预期负担，以补充目前存在的州和国家级建模框架。一群不同的学生将通过COVID-19的Covid-19和Flusight的COVID预测中心和流感的Flusight进行模型开发和将结果传播给CDC，并进一步扩展了既定的学术政府合作伙伴关系。公开访问的提交和制作的整体预测将有助于增强社会对感染性疾病风险的一般理解，并有助于提高公众的科学翻译和识字率。住院预测模型将利用机械模型和统计数据驱动的方法，以将差异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型建模技术，以进一步提高预测能力。高分辨率，即社区和城市层面，模型的性质将填补文献和实践的差距，迄今为止，这是由州和国家一级的预测所占据主导地位的。高度局部，更可行的空间尺度都将增加我们在实践中模型的效用，并为地方官员提供了区分跨人群危害的机制，从而为决策者提供了更公平，公平的政策指导。明确考虑问题上下文的新颖评估指标的开发将进一步增加我们的模型的效用，并为更广泛的建模社区提供一套新的绩效工具。从长远来看，我们进行研究工作的系统工程方法将有助于建立可靠的，可用于跨多个变量的预测，在呼吸道病毒疾病和大流行期的季节性周期中，该工具可用于预测，这奖项反映了NSF的法定范围，反映了通过评估范围的支持者的支持者，并已被评估范围的商品范围和众所周知的范围。