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

基本信息

批准号：
10689034
负责人：
Nicholas G Reich
金额：
$ 43.21万
依托单位：
UNIVERSITY OF MASSACHUSETTS AMHERST
依托单位国家：
美国
项目类别：
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

项目摘要

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。

项目成果

期刊论文数量（37）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Estimating the early death toll of COVID-19 in the United States.

DOI：
10.1101/2020.04.15.20066431
发表时间：
2020-04-29
期刊：
medRxiv : the preprint server for health sciences
影响因子：
0
作者：
Weinberger, Daniel M;Cohen, Ted;Viboud, Cecile
通讯作者：
Viboud, Cecile

Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases.

量化传染病主动监测的风险和成本。

DOI：
10.1038/s41598-018-19406-x
发表时间：
2018
期刊：
Scientific reports
影响因子：
4.6
作者：
Reich,NicholasG;Lessler,Justin;Varma,JayK;Vora,NeilM
通讯作者：
Vora,NeilM

Improving probabilistic infectious disease forecasting through coherence.

DOI：
10.1371/journal.pcbi.1007623
发表时间：
2021-01
期刊：
PLoS computational biology
影响因子：
4.3
作者：
Gibson GC;Moran KR;Reich NG;Osthus D
通讯作者：
Osthus D

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.

DOI：
10.1016/j.ijforecast.2022.06.005
发表时间：
2023-07
期刊：
INTERNATIONAL JOURNAL OF FORECASTING
影响因子：
7.9
作者：
Ray, Evan L.;Brooks, Logan C.;Bien, Jacob;Biggerstaff, Matthew;Bosse, Nikos I.;Bracher, Johannes;Cramer, Estee Y.;Funk, Sebastian;Gerding, Aaron;Johansson, Michael A.;Rumack, Aaron;Wang, Yijin;Zorn, Martha;Tibshirani, Ryan J.;Reich, Nicholas G.
通讯作者：
Reich, Nicholas G.

Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.

评估 COVID-19 病例报告作为美国住院预测领先指标的效用。