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Influenza Forecasting Center of Excellence at University of Massachusetts Amherst

马萨诸塞大学阿默斯特分校流感预测卓越中心

基本信息

批准号：
10642728
负责人：
Nicholas G Reich
金额：
$ 95万
依托单位：
UNIVERSITY OF MASSACHUSETTS AMHERST
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10642728
关键词：
Influenza Forecasting Center Excellence University

项目摘要

PROJECT SUMMARY/ABSTRACT The central goal of this project is to serve as a strong, interdisciplinary center of forecasting research and science. We will achieve this through innovation in development of forecasting methodologies and systematic research into optimal the communication and visualization of forecasts. By working with the US Centers for Disease Control and Prevention in this cooperative agreement, we aim to extend existing methodologies to incorporate new data sources and model structures, thereby improving influenza forecast accuracy in the US. We will review and revise existing FluSight forecasting guidance, targets, and accuracy evaluation at the national, regional, and state levels. This will include conducting stakeholder interviews with federal, state, and local epidemiologists to track uses of forecasts and outcomes and identifying unmet needs from current forecasts. We will develop and refine methods to create forecast ensembles, with a specific focus on developing ensemble weighting schemes using robust, penalized methods to estimate model weights. We will identify methodologies and data sources that increase forecast accuracy for start and peak week forecasts, peak intensity, and short-term forecasts at the national, regional, and state level. Our work here will focus on developing multi-scale spatial models that leverage state and zip-code level data on influenza infections from public and private sources. We will develop communication products and methods to describe forecast results and uncertainty for federal and state public health officials and the public. We will achieve this by incorporating new visualizations into our existing interactive data visualization product for influenza forecasts in the US and studying systematically the end-user perception of various visualization and data presentation layouts. Finally, we will develop and adapt successful seasonal methodologies, data sources, and communication approaches for forecasting the timing, intensity, and short-term trajectory of an emerging influenza pandemic. Specifically, we will create, test, and disseminate weekly data summaries and visualizations from the most up-to-date sources of reported influenza cases in the US (including data from our real-time point-of-care data sources), and validate our new spatial models against simulated pandemic data.

项目总结/文摘