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: 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
• 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/10689034
• 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.

项目总结

项目成果

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

The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research.

• DOI: 10.1038/s41597-021-00839-5
• 发表时间: 2021-02-11
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Reich NG;Cornell M;Ray EL;House K;Le K
• 通讯作者: Le K

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.

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

Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action.

• DOI: 10.1016/j.epidem.2020.100400
• 发表时间: 2020-12
• 期刊: Epidemics
• 影响因子: 3.8
• 作者: Pollett S;Johansson M;Biggerstaff M;Morton LC;Bazaco SL;Brett Major DM;Stewart-Ibarra AM;Pavlin JA;Mate S;Sippy R;Hartman LJ;Reich NG;Maljkovic Berry I;Chretien JP;Althouse BM;Myer D;Viboud C;Rivers C
• 通讯作者: Rivers C