Development and Dissemination of Operational Real-Time Respiratory Virus Forecast

实时呼吸道病毒预测的开发和传播

• 批准号: 8703891
• 负责人: JEFFREY L SHAMAN
• 金额: $ 51.07万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8703891
• 关键词: Address; Adenoviruses; Affect; Assimilations; Awareness; Biology; Characteristics; Cities; Collaborations; Communicable Diseases; Computing Methodologies; Data; Decision Making; Development; Discrimination; Disease; Disease Outbreaks; Disease Outcome; Effectiveness; Ensure; Epidemiology; Future; Goals; Health; Human; Incidence; Individual; Infection; Infectious Diseases Research; Influenza; Internet; Intervention; Lead; Light; Lung diseases; Measures; Mental Health; Metapneumovirus; Methods; Modeling; Neighborhoods; New York City; Outcome; Parainfluenza; Population Dynamics; Probability; Process; Public Health; Quarantine; Readiness; Recurrence; Research; Research Infrastructure; Resources; Respiratory syncytial virus; Rotavirus; Running; Schools; Seasons; Severities; Statistical Methods; Statistical Models; System; Techniques; Testing; Therapeutic; Time; Training; United States; Vaccination; Viral; Virus; Weather; Work; base; disease transmission; epidemiological model; face mask; flu transmission; human morbidity; human mortality; improved; influenza outbreak; mathematical methods; mathematical model; meetings; models and simulation; operation; pathogen; research and development; respiratory; respiratory virus; response; seasonal influenza; simulation; syndromic surveillance; transmission process; user-friendly; verification and validation

Recurrent outbreaks of influenza and other respiratory viruses continue to affect human health adversely. A number of intervention strategies exist to mitigate the progression of these pathogens, including vaccination, anti-viral therapeutics, public awareness campaigns, face masks, school closure, and quarantine. Public health agency use of these control strategies is guided by their historical effectiveness and implemented in light of the latest estimates of infection incidence, severity, and transmissibility; however, public health officials would be afforded more time to allocate their intervention measures if local outbreak characteristics, e.g., incidence timing, magnitude and duration, could be accurately and reliably forecast. Recent work has shown that some characteristics of seasonal influenza outbreaks can be predicted accurately with lead times of up to 9 weeks. These predictions are generated with a mathematical model of influenza transmission dynamics that has been recursively optimized using an ensemble data assimilation technique and real-time observations of infection incidence. In practice, the data assimilation process entrains the observational estimates of infection incidence into evolving mathematical simulations of pathogen transmission dynamics, and in so doing trains those model simulations, through state space estimation and parameter optimization, to better match the observed unfolding outbreak. Those trained simulations, having been optimized with the most recent observations, are then integrated into the future to generate a distribution of potential disease outcomes. This forecasting framework has been validated for accuracy and reliability, and during the 2012-2013 influenza season was used to generate weekly real-time predictions of influenza peak timing for 108 cities throughout the United States. For this project, we will build on and expand these forecast efforts. Specifically, we will: 1) Work to improve influenza forecast accuracy and reliability through development of multi-model forecast approaches, such as have been used in weather prediction; 2) Develop, test and analyze analogous forecast frameworks for other recurrent respiratory pathogens, such as rotavirus and respiratory syncytial virus; 3) Establish a dedicated operation center for maintaining, running and disseminating real-time weekly forecasts of influenza and other respiratory viruses; and 4) Work with public health officials in New York City, and, using their more detailed syndromic surveillance, explore the potential for more granular, borough or neighborhood-scale forecast of influenza and other viruses. These efforts will lead to an improved understanding of the benefits and limits of respiratory disease prediction, and the intelligent interpretation and incorporation of real-time forecasts in health response decision-making.

流感和其他呼吸道病的反复爆发继续对人类健康不利影响。一个 存在一些干预策略来减轻这些病原体的发展，包括 疫苗接种，抗病毒治疗学，公众意识运动，面具，封闭学校和 隔离。公共卫生机构使用这些控制策略的历史有效性指导 并根据感​​染发生率，严重性和传播性的最新估计； 但是，如果本地，公共卫生官员将有更多时间分配干预措施 爆发特征，例如发病时间，幅度和持续时间，可以准确且可靠 预报。最近的工作表明，季节性流感暴发的某些特征可能是 准确预测的交货时间长达9周。这些预测是通过数学生成的 使用集合数据递归优化的流感传播动力学模型 感染发生率的同化技术和实时观察结果。实际上，数据同化 过程将感染发生率的观察性估计值纳入不断发展的数学模拟 病原体传播动力学，因此，通过状态空间进行训练这些模型模拟 估计和参数优化，以更好地匹配观察到的展开爆发。那些受过训练的人 通过与最新观测值进行了优化的模拟，然后将其集成到未来 产生潜在疾病结局的分布。这个预测框架已被验证 准确性和可靠性，以及在2012-2013的流感季节，用于生成每周实时的 全美国108个城市的流感峰值时间的预测。对于这个项目，我们将建立 继续并扩大这些预测的努力。具体来说，我们将：1）提高流感预测准确性 通过开发多模型预测方法的可靠性，例如在天气中使用 预言; 2）开发，测试和分析其他复发性呼吸的类似预测框架 病原体，例如轮状病毒和呼吸道合胞病毒； 3）建立一个专门的运营中心 维持，运行和传播对流感和其他呼吸系统的实时每周预测 病毒； 4）与纽约市的公共卫生官员合作，并使用其更详细的综合症 监视，探索更多颗粒状，自治市镇或邻里尺度的预测 和其他病毒。这些努力将导致人们对利益和限制的了解 呼吸道疾病预测以及实时预测的智能解释和纳入 健康反应决策。