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

> **NIH NIH R35** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2021 · $432,859

## Abstract

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.

## Key facts

- **NIH application ID:** 10205685
- **Project number:** 2R35GM119582-06
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Nicholas G Reich
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $432,859
- **Award type:** 2
- **Project period:** 2016-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10205685

## Citation

> US National Institutes of Health, RePORTER application 10205685, Methods for real-time forecasting and inference during infectious disease outbreaks (2R35GM119582-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10205685. Licensed CC0.

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