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

传染病爆发期间的实时预测和推断方法

• 批准号: 10205685
• 负责人: Nicholas G Reich
• 金额: $ 43.29万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10205685
• 关键词: Area; Collection; Communicable Diseases; Communities; Data; Decision Making; Disease Outbreaks; Evaluation; Goals; Health; Healthcare; Individual; Intervention; Learning; Learning Module; Methodology; Methods; Modernization; Population; Prevention strategy; Public Health; Research; Research Activity; Research Personnel; Series; Statistical Models; Time; Training; Vaccine Design; analytical method; global health; improved; infectious disease model; open source; prevent; vaccine trial

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.

项目摘要 21世纪科学界的一个根本挑战是学习如何转变这一洪水 数据成为可以告知有关改善健康和预防疾病的决策的证据 个人和人口水平。实时传染病预测的成熟领域是主要的 具有极大潜力的研究领域的示例，以利用现代分析方法来最大化 对公共卫生的影响。每年，传染病会给全球健康带来巨大的损失。改进的房地产 传染病暴发的时间预测可以为有针对性的干预和预防策略提供信息，例如 作为激发能力的计划，增加医疗保健人员配备以及设计疫苗研究。但是我们 目前对将这些类型的预测整合到实时的最佳方法有限 公共卫生决策。该项目的中心研究活动是（1）开发独立的和 合奏感染疾病模型和方法学，支持预测和推断 爆发和（2）扩展我们的协作，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，我们将继续开发一套开放式 来源教育模块，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。