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

> **NIH NIH R35** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2020 · $78,507

## Abstract

PROJECT SUMMARY
The emergence and global expansion of SARS-CoV-2 as a human pathogen over the last four months
represents a nearly unprecedented challenge for the infectious disease modelling community. This pandemic
has benefitted from huge volumes of data being generated, but the rate of dissemination of these data has
often outpaced existing data pipelines. While the last decade has seen significant advances in real-time
infectious disease forecasting — spurred by rapid growth in data and computational methods — these
methods have primarily focused on seasonal endemic diseases based, are based on historical data, and so
do not apply easily to this novel pathogen, or to pandemic scenarios. New methods are needed to leverage
the wealth of surveillance data at fine spatial granularity, together with associated information about policy
interventions and environmental conditions over space and time, to reason directly about the mechanisms to
forecast and understand the transmission dynamics of SARS-CoV-2 transmission. These methods must use
sound statistical and epidemiological principles and be flexible and computationally efficient to provide real-
time forecasts to guide public health decision-making and respond to changing aspects of this global crisis.
The central research activities of this project are (1) to develop scalable, computationally efficient Bayesian
hierarchical compartmental models to flexibly respond to state-level public health forecasting needs, and (2)
to design models and conduct analyses to draw robust inference about the effectiveness of interventions in
impacting the reproductive rate of SARS-CoV-2 infections within the US to build an evidence-base for
continued responses to COVID-19 and future pandemics.

## Key facts

- **NIH application ID:** 10150377
- **Project number:** 3R35GM119582-04S1
- **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:** $78,507
- **Award type:** 3
- **Project period:** 2016-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10150377

## Citation

> US National Institutes of Health, RePORTER application 10150377, Statistical methods for real-time forecasts of infectious disease: expanding dynamic time-series and machine learning approaches for pandemic scenarios (3R35GM119582-04S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10150377. Licensed CC0.

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