Development and Dissemination of Operational Real-Time Respiratory Virus Forecast

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 目前有许多干预策略可以减缓这些病原体的进展，包括 疫苗接种、抗病毒治疗、公众宣传活动、口罩、学校关闭， 检疫.公共卫生机构对这些控制策略的使用以其历史有效性为指导 并根据对感染发生率、严重程度和传染性的最新估计加以实施; 然而，如果地方政府采取干预措施， 爆发特征，例如，事件发生的时间，大小和持续时间，可以准确可靠地 预报.最近的研究表明，季节性流感暴发的一些特征可能是 预测准确，交货期长达9周。这些预测是由一个数学 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 这一过程将感染发生率的观测估计带入了不断发展的数学模拟中 病原体传播动力学，并在这样做的训练这些模型模拟，通过状态空间 估计和参数优化，以更好地匹配观察到的展开爆发。培训的人 然后，利用最近的观测结果对模拟进行优化，并将其集成到未来， 产生潜在疾病结果的分布。这一预测框架已得到验证， 准确性和可靠性，并在2012-2013年流感季节用于生成每周实时 对全美108个城市流感高峰时间的预测。在这个项目中，我们将建立 并扩大这些预测工作。具体而言，我们将：1）努力提高流感预测的准确性 通过发展多模式预报方法，例如在天气预报中使用的方法， 预测; 2）开发、测试和分析其他复发性呼吸道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， 维持、运行和传播流感和其他呼吸系统疾病的实时每周预报 4）与纽约市的公共卫生官员合作，并使用他们更详细的症状 监测，探索对流感进行更精细、自治市或社区规模预测的可能性 和其他病毒。这些努力将使人们更好地了解 呼吸道疾病预测，以及智能解释和实时预测的结合， 卫生应急决策。

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.