Delphi Influenza Forecasting Center of Excellence

德尔福流感预测卓越中心

• 批准号: 10650191
• 负责人: Ronald Rosenfeld
• 金额: $ 95万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10650191
• 关键词: Delphi; Influenza; Forecasting; Center; Excellence

ABSTRACT: There is a critical need for Influenza forecasting among public health decision makers, large organizations, healthcare participants, and the general public. We focus in this proposal on the first and last categories. For federal and state officials, influenza forecasting can help inform the timing of critical communications, vaccination campaigns, messaging to state and local public health agencies, hospitals, healthcare professionals, and the public. During flu pandemics, forecasting can inform the formulation and execution of strategies for vaccine development, vaccine distribution and (application), dissemination of antivirals, and recommendations for non-pharmaceutical interventions. Further, public health communications to healthcare providers can result in more informed doctors’ decisions regarding use of antibiotics and hospitalization. For the general public, reliable short term forecasts can increase the credibility of and trust in pubic health authorities, resulting in greater adherence to recommendations. Short term forecasts can also inform individuals’ decision making, especially with regard to behavior and exposure of flu-vulnerable populations: the elderly, the very young, the pregnant and the immunocompromised. Members of these groups may reconsider their travel plans and other public exposure if warned in advance of an impending epidemic wave at a specific location. While such decisions might also be informed by existing flu surveillance, inherent latencies in traditional surveillance means that nowcasting and short-term forecasting provide a few weeks advance notice -- enough to save many lives. To advance the state of the art in influenza forecasting, we propose an Influenza Forecasting Center of Excellent that will enable and improve the usefulness of forecasts of both seasonal influenza and pandemic influenza, to inform public health responses and policy development at the national, regional, and state level. The center will (1) review and, if needed, revise existing forecasting guidance, targets, and accuracy evaluation, at the national, regional, and state levels; (2) refine methods to create forecast ensembles; (3) identify methodologies and data sources that increase forecast accuracy for start and peak week forecasts, peak intensity, and short-term forecasts at the national, regional, and state level; (4) develop communication products and data visualization methods to describe forecast results and uncertainty for federal and state public health officials and the public; and (5) develop and adapt successful seasonal methodologies, data sources, and communication approaches for forecasting the timing, intensity, and short-term trajectory of an emerging influenza pandemic.

摘要： 在公共卫生决策者、大型组织、 医疗保健参与者和普通公众。在本提案中，我们将重点放在第一类和最后一类。 对于联邦和州官员来说，流感预报可以帮助确定关键通信的时间， 疫苗接种活动，向州和地方公共卫生机构、医院、医疗保健发送消息 专业人士和公众。在流感大流行期间，预测可以为制定和执行 疫苗开发、疫苗分发和(应用)、抗病毒药物传播战略，以及 对非药物干预的建议。此外，公共卫生与医疗保健的沟通 提供者可以使医生在使用抗生素和住院方面做出更明智的决定。 对于普通公众来说，可靠的短期预测可以增加公众健康的可信度和信任度 因此，更多地遵守了各项建议。短期预测也可以提供信息 个人决策，特别是关于流感易感人群的行为和暴露： 老年人、非常年轻的人、孕妇和免疫力受损的人。这些团体的成员可能会重新考虑 如果在特定地点提前警告即将到来的疫情浪潮，他们的旅行计划和其他公开接触 地点。虽然这样的决定可能也会受到现有的流感监测的影响，但 传统的监测意味着现在预报和短期预报会提前几周发出通知。 --足以拯救许多人的生命。 为了提高流感预测的技术水平，我们提出了一个流感预测中心， 很好，这将使季节性流感和大流行的预测变得更加有用 流感，为国家、地区和州一级的公共卫生应对和政策制定提供信息。 该中心将(1)审查并在必要时修改现有的预测指南、目标和准确性 在国家、区域和州各级进行评估；(2)改进方法以创建预报集合；(3) 确定可提高开工周和高峰周预报准确性的方法和数据来源， 国家、地区和州一级的峰值强度和短期预报；(4)发展沟通 产品和数据可视化方法，用于描述联邦和州的预测结果和不确定性 公共卫生官员和公众；以及(5)制定和采用成功的季节性方法、数据 来源和通信方法，用于预测时间、强度和短期轨迹 新出现的流感大流行。

项目成果

Time trends, factors associated with, and reasons for COVID-19 vaccine hesitancy: A massive online survey of US adults from January-May 2021.

• DOI: 10.1371/journal.pone.0260731
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: King WC;Rubinstein M;Reinhart A;Mejia R
• 通讯作者: Mejia R

Epidemic tracking and forecasting: Lessons learned from a tumultuous year.

• DOI: 10.1073/pnas.2111456118
• 发表时间: 2021-12-21
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Rosenfeld R;Tibshirani RJ
• 通讯作者: Tibshirani RJ

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

• DOI: 10.1073/pnas.2111453118
• 发表时间: 2021-12-21
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: McDonald DJ;Bien J;Green A;Hu AJ;DeFries N;Hyun S;Oliveira NL;Sharpnack J;Tang J;Tibshirani R;Ventura V;Wasserman L;Tibshirani RJ
• 通讯作者: Tibshirani RJ

Recalibrating probabilistic forecasts of epidemics.

• DOI: 10.1371/journal.pcbi.1010771
• 发表时间: 2022-12
• 期刊: PLoS computational biology
• 影响因子: 4.3

An open repository of real-time COVID-19 indicators.

• DOI: 10.1073/pnas.2111452118
• 发表时间: 2021-12-21
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Reinhart A;Brooks L;Jahja M;Rumack A;Tang J;Agrawal S;Al Saeed W;Arnold T;Basu A;Bien J;Cabrera ÁA;Chin A;Chua EJ;Clark B;Colquhoun S;DeFries N;Farrow DC;Forlizzi J;Grabman J;Gratzl S;Green A;Haff G;Han R;Harwood K;Hu AJ;Hyde R;Hyun S;Joshi A;Kim J;Kuznetsov A;La Motte-Kerr W;Lee YJ;Lee K;Lipton ZC;Liu MX;Mackey L;Mazaitis K;McDonald DJ;McGuinness P;Narasimhan B;O'Brien MP;Oliveira NL;Patil P;Perer A;Politsch CA;Rajanala S;Rucker D;Scott C;Shah NH;Shankar V;Sharpnack J;Shemetov D;Simon N;Smith BY;Srivastava V;Tan S;Tibshirani R;Tuzhilina E;Van Nortwick AK;Ventura V;Wasserman L;Weaver B;Weiss JC;Whitman S;Williams K;Rosenfeld R;Tibshirani RJ
• 通讯作者: Tibshirani RJ