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

该项目将为2023/2024季节的COVID、流感和RSV发病率和严重程度开发新模型和预测。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工预测结合起来，寻求提高预测准确性和响应能力。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度和透明度。该项目将通过提供每周预测，分析和沟通支持，加强中心对2023/2024呼吸道疾病季节的准备。公众沟通将通过CDC准备的数据仪表板进行，并填充项目预测，以及每周书面更新，这些将共同提供关于COVID，流感和RSV的预期时间，严重程度和峰值的全面，定期更新的概率前景。该项目将与CDC密切合作，制定量身定制的视觉和口头沟通策略，强调预测信心。该提案将研究频繁的概率预测和相关的公共沟通的潜力，以提高全国范围内在呼吸道疾病高峰季节的公共卫生准备，建立在成功的州一级流行病学应对和规划工作的基础上，从2020年至2023年。该项目将举办一场公共预测锦标赛，提供数值预测，用于沟通和量化RSV、流感和COVID的预期秋冬呼吸道疾病负担。它将与疾病预防控制中心密切合作，为锦标赛创建预测问题。关注呼吸道疾病发病高峰的时间和程度的问题将有助于绘制预期的轨迹，而其他问题可能会跟踪疫苗接种，每周的疾病负担，新出现的变异的可能性以及高医院利用率的风险。通过与CDC的合作，它还将探索预测问题的潜力，这些问题可用于通过人类判断和参数化来增强现有的CDC建模。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.