# Automated Surveillance of Overlapping Outbreaks and New Outbreak Diseases

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $337,875

## Abstract

Project Summary / Abstract
This project will develop and evaluate new methods for automated detection and characterization of infectious
respiratory diseases. The methods will be novel in their ability to detect and characterize (1) multiple,
overlapping outbreaks of known diseases, which is a situation that occurs commonly, (2) an outbreak of a new,
emerging disease, which can be dangerous, and (3) a combination of 1 and 2 occurring at the same time. The
ability to detect a new disease early, in the context of other common outbreaks occurring, may be particularly
important if the disease causes serious illness and spreads rapidly in the population. The new methods can
also use a wide variety of data to perform outbreak detection and characterization, including emergency
department reports, laboratory results, retail thermometer sales in the region, and local health-related tweets.
These new methods will be built upon the framework of an existing Bayesian, probabilistic system, which the
investigators have developed. This system takes as input data used to perform outbreak detection and
characterization, and it outputs the probabilities of different possible disease outbreaks that may be occurring,
as well as their characteristics, such as their probable start times and epidemiological curves. A unique aspect
of the system is its ability to use data from individual patient clinical reports, such as emergency department
reports. The system applies natural language processing to the reports to derive a set of symptoms, signs, and
other findings. It then uses these findings and probabilistic disease models to derive a probability distribution
over the diseases for each patient. For the many patients seen in the recent past, the system uses their
probability distributions as evidence in detecting and characterizing disease outbreaks.
The project will be evaluated using simulated data and real data from Allegheny County, Pennsylvania. It will
focus on four common outbreak diseases, namely, influenza A, influenza B, respiratory syncytial virus (RSV),
and adenovirus. The evaluation will examine how well the system can (1) detect and characterize multiple
overlapping outbreaks of disease, (2) detect a new outbreak disease and create an accurate clinical
description of it (using a leave-one-out cross validation approach), and (3) use a variety of data types to
improve outbreak detection and characterization.
The innovation being advanced by this research is a novel, integrated, probabilistic approach for the early and
accurate detection of disease outbreaks that threaten public health. The proposed approach has significant
potential to improve the information available to clinicians and public health officials, which can be expected to
improve clinical and public health decision making, and ultimately to improve population health.

## Key facts

- **NIH application ID:** 10460909
- **Project number:** 5R01LM013509-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** GREGORY F. COOPER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $337,875
- **Award type:** 5
- **Project period:** 2021-08-03 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10460909

## Citation

> US National Institutes of Health, RePORTER application 10460909, Automated Surveillance of Overlapping Outbreaks and New Outbreak Diseases (5R01LM013509-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10460909. Licensed CC0.

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