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.

抽象的： 在公共卫生决策者，大型组织， 医疗保健参与者和公众。我们将重点放在第一个和最后类别上。 对于联邦和州官员，Intralena的预测可以帮助告知关键沟通的时机， 疫苗接种运动，向州和地方公共卫生机构，医院，医疗保健的消息传递 专业人士和公众。在流感大流传学期间，预测可以告知公式和执行 疫苗开发，疫苗分布和（应用），抗病毒药的传播策略以及 针对非药物干预的建议。此外，通往医疗保健的公共卫生通讯 提供者可能会导致更多知情的医生在使用抗生素和住院治疗方面的决定。 对于普通大众来说，可靠的短期森林可以提高公民健康的信誉和信任 当局，导致更大的建议。短期预测也可以告知 个人的决策，尤其是在行为和可流感造成的人群的暴露方面： 老年人，非常年轻，怀孕和免疫功能低下。这些小组的成员可能会重新考虑 如果在某个特定的流行浪潮之前警告他们的旅行计划和其他公众暴露 地点。尽管现有的流感监测也可能会告知此类决定，但 传统的监视意味着现实和短期预测会提前几周通知 - 足以挽救许多生命。 为了促进影响力的预测状况，我们提出了一个流感的预测中心 优秀的季节性影响和大流行的森林的实用性将使森林的实用性 影响力，以告知国家，地区和州一级的公共卫生反应和政策制定。 该中心将（1）审查，并在需要时修改现有的预测指导，目标和准确性 在国家，地区和州一级的评估； （2）完善的方法来创建预测合奏； （3） 确定提高开始和高峰周森林的预测准确性的方法和数据源， 国家，地区和州一级的峰值强度以及短期森林； （4）发展沟通 产品和数据可视化方法来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； （5）开发和调整成功的季节性方法，数据 来源和沟通方法，以预测一个时间，强度和短期轨迹 新兴影响者大流行。

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