RAPID: Forecasting & Communicating the US Fall/Winter Respiratory Disease Outlook

该项目将为2023/2024年季节开发新的COVID、流感和RSV发病率和严重程度的模型和预测。该项目将通过将计算模型与人类判断相结合来测试一种新的公共卫生预测方法。它将首次在全国范围内将计算建模和人类预测结合在一起，寻求提高预测的准确性和响应性。此外，它还将通过引入创新的沟通方法来明确传达风险和不确定性，从而提高公众的信任和透明度。该项目将通过提供每周预报、分析和通信支持，加强该中心对2023/2024年呼吸道疾病季节的准备工作。公众交流将通过疾控中心准备的数据仪表板以及每周书面更新提供，这些数据仪表板上填满了项目预测，这些更新将提供关于COVID、流感和RSV的预期时间、严重性和高峰的全面、定期更新的概率展望。在与疾控中心的密切合作下，该项目将制定量身定制的视觉和语言沟通策略，强调预测信心。这项建议调查了频繁、概率预测和相关公共沟通的潜力，以2020-2023年成功的州级流行病学应对和规划工作为基础，在呼吸道疾病高峰期改善全国公共卫生准备工作。该项目将举办一场公众预报竞赛，以提供数字预报，用于传达和量化预计的RSV、流感和COVID秋季和冬季呼吸道疾病负担。它将与疾控中心密切合作，为锦标赛制作预测问题。集中在呼吸道疾病发病高峰的时间和规模的问题将有助于绘制预期的轨迹，而其他问题可能跟踪疫苗接种量、每周的疾病负担、出现新变种的可能性以及医院利用率高的风险。与疾控中心合作，它还将探索预测问题的潜力，这些问题可以通过人工判断和参数化来增强现有的疾控中心建模。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop new models and forecasts of COVID, influenza, and RSV incidence and severity for the 2023/2024 season. The project will test a new approach to public health forecasting by combining computational models with human judgment. For the first time on a national scale, it will bring together computational modeling and human forecasting, seeking to improve forecasting accuracy and responsiveness. Additionally, it will boost public trust and transparency by introducing innovative communication methods to clearly convey risk and uncertainty. The project will bolster the preparedness of the Center for the 2023/2024 respiratory disease season through the delivery of weekly forecasts, analysis, and communication support. Public communication will be delivered via data dashboards prepared by the CDC and populated with project forecasts, as well as weekly written updates that together will provide a comprehensive, regularly updated, probabilistic outlook on the expected timing, severity, and peaks of COVID, influenza, and RSV. In close collaboration with the CDC, the project will craft tailored visual and verbal communication strategies, emphasizing forecast confidence.This proposal investigates the potential of frequent, probabilistic forecasting and related public communication to improve nationwide public health preparedness during the peak respiratory disease season, building on successful state-level epidemiological response and planning efforts from 2020-2023. The project will operate a public forecasting tournament to provide numerical forecasts for use in communicating and quantifying the expected fall and winter respiratory disease burden of RSV, influenza, and COVID. It will work closely with the CDC to create forecasting questions for the tournament. Questions focused on the timing and magnitude of the peak onset of respiratory illnesses will help chart the expected trajectory, while other questions may track vaccine uptake, week-by-week disease burden, the potential for emerging variants, and the risk of high hospital utilization. In collaboration with the CDC, it also will explore the potential for forecasting questions that could be used to enhance existing CDC modeling through human judgment and parameterization.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.