# Statistical methods for real-time forecasts of infectious disease: dynamic time-series and machine learning approaches

> **NIH NIH R35** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2020 · $593,966

## Abstract

PROJECT SUMMARY
The past decade of biomedical research has borne witness to rapid growth in data and computational methods.
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 emerging 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 increased healthcare staffing or vector control measures. 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 and validate a suite of robust, real-time
statistical prediction models for infectious diseases, (2) we will develop and evaluate an ensemble time-series
prediction methodology for integrating multiple prediction models into a single forecast, and (3) to develop a
collaborative platform for dissemination and evaluation of predictions by different research teams. Additionally,
we will 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:** 10002249
- **Project number:** 5R35GM119582-05
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Nicholas G Reich
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $593,966
- **Award type:** 5
- **Project period:** 2016-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002249

## Citation

> US National Institutes of Health, RePORTER application 10002249, Statistical methods for real-time forecasts of infectious disease: dynamic time-series and machine learning approaches (5R35GM119582-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10002249. Licensed CC0.

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