# Influenza Forecasting Center of Excellence at University of Massachusetts Amherst

> **NIH ALLCDC U01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2022 · $950,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10460892
- **Project number:** 5U01IP001122-04
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Nicholas G Reich
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2022
- **Award amount:** $950,000
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10460892

## Citation

> US National Institutes of Health, RePORTER application 10460892, Influenza Forecasting Center of Excellence at University of Massachusetts Amherst (5U01IP001122-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10460892. Licensed CC0.

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