Methods for real-time forecasting and inference during infectious disease outbreaks

项目总结 21世纪科学界面临的一个根本挑战是学习如何将这种洪流 将数据转化为证据，为有关改善健康和预防疾病的决策提供信息 个人和人口水平。日益成熟的实时传染病预测领域是最好的 具有利用现代分析方法最大限度地提高 对公众健康的影响。传染病每年对全球健康造成巨大损失。改进的实际- 传染病暴发的时间预测可以为有针对性的干预和预防战略提供信息，例如 如规划激增能力，增加医疗保健人员，以及设计疫苗研究。然而，我们 目前对将这些类型的预报集成到实时中的最佳方法的了解有限 公共卫生决策。本项目的中心研究活动是(1)开发独立的和 支持预测和推断传染病的综合模型和方法 疫情和(2)扩大我们的协作在线平台，用于收集、传播、评估和 综合不同研究团队的预测。此外，我们还将继续开发一套开放的 获取教育模块，以培训研究人员和公共卫生官员开发、验证和 实施时间序列预测，重点是实时传染病应用。

PROJECT SUMMARY A fundamental challenge for the scientific community in the 21st century is learning how to turn this deluge of data into evidence that can inform decision-making about improving health and preventing illness at the individual and population levels. The maturing field of real-time infectious disease forecasting is a prime example of a research area with great potential for leveraging modern analytical methods to maximize the impact on public health. Infectious diseases exact an enormous toll on global health each year. Improved real- time forecasts of infectious disease outbreaks can inform targeted intervention and prevention strategies, such as planning for surge capacity, increasing healthcare staffing, and designing vaccine studies. However we currently have a limited understanding of the best ways to integrate these types of forecasts into real-time public health decision-making. The central research activities of this project are (1) to develop stand-alone and ensemble infectious disease models and methodologies that support forecasting and inference about outbreaks and (2) to expand our collaborative, online platform for collection, dissemination, evaluation, and synthesis of forecasts from different research teams. Additionally, we will continue to develop a suite of open- source educational modules to train researchers and public health officials in developing, validating, and implementing time-series forecasting, with a focus on real-time infectious disease applications.