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: 9306882
• 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/9306882
• 关键词: Address; Adenoviruses; Affect; Assimilations; Awareness; Biology; Characteristics; Cities; Collaborations; Communicable Diseases; Competence; 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; Intervention; Lead; Light; Lung diseases; Mathematics; 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; influenza surveillance; mathematical methods; mathematical model; models and simulation; operation; pathogen; research and development; respiratory; respiratory virus; response; seasonal influenza; simulation; syndromic surveillance; transmission process; user-friendly; verification and validation; web portal

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毒的反复爆发继续对人类健康产生不利影响。一个 存在多种干预策略来减缓这些病原体的进展，包括 疫苗接种、抗病毒治疗、公众意识运动、口罩、学校停课以及 隔离。公共卫生机构对这些控制策略的使用以它们的历史有效性为指导 并根据感​​染发生率、严重程度和传播性的最新估计来实施； 然而，如果当地能够采取行动，公共卫生官员将有更多时间来分配干预措施 可以准确可靠地了解爆发特征，例如发生时间、规模和持续时间 预报。最近的工作表明，季节性流感爆发的一些特征可以通过 准确预测，交货时间长达 9 周。这些预测是通过数学计算生成的 使用集合数据递归优化的流感传播动力学模型 同化技术和感染发生率的实时观察。在实践中，数据同化 该过程将感染发生率的观察估计纳入不断发展的数学模拟中 病原体传播动力学，并通过状态空间训练这些模型模拟 估计和参数优化，以更好地匹配观察到的正在发生的疫情。那些经过培训的 模拟根据最近的观察进行了优化，然后被集成到未来中 生成潜在疾病结果的分布。该预测框架已经过验证 准确性和可靠性，并在 2012-2013 年流感季节期间用于生成每周实时数据 预测美国 108 个城市的流感高峰时间。对于这个项目，我们将构建 继续并扩大这些预测工作。具体来说，我们将： 1）努力提高流感预测的准确性 通过开发多模型预报方法（例如在天气中使用的方法）来提高可靠性 预言; 2) 开发、测试和分析其他复发性呼吸系统疾病的类似预测框架 病原体，如轮状病毒和呼吸道合胞病毒； 3）建立专门的运营中心 维护、运行和传播流感和其他呼吸道疾病的每周实时预报 病毒； 4) 与纽约市的公共卫生官员合作，并利用他们更详细的症状 监测，探索更精细、行政区或社区规模的流感预测的潜力 和其他病毒。这些努力将导致人们更好地了解 呼吸系统疾病预测，以及实时预测的智能解释和整合 健康应对决策。