# Health Informatics to Model the Scott County HIV Outbreak

> **NIH NIH R21** · UNIVERSITY OF GEORGIA · 2020 · $198,078

## Abstract

ABSTRACT
Many regions of the United States, particularly rural and frontier areas, lack the resources to proactively
identify isolated infectious disease outbreaks. Scott County Indiana experienced an HIV outbreak in 2014-
2015, resulting in approximately 200 new HIV infections. If not for a vigilant Disease Intervention Specialist
(DIS) who noticed an escalating number of HIV infections in Scott County over a brief period of time, the
outbreak could have been much worse. An isolated outbreak in resource-limited settings such as Scott County
underscores the need for more innovative, automated, and real-time HIV biosurveillance systems in non-urban
areas. To date, digital HIV epidemiologic research has relied almost exclusively on Twitter; however, this
approach is likely too restrictive and has not yet yielded a promising approach to predicting HIV outbreaks.
More heterogeneous sources of data--in addition to social media--may more efficiently predict the arrival of HIV
in a community with limited surveillance resources. The proposed health informatics research will analyze
historical time series data collected from 2014 through 2016 to identify predictor variables that model the Scott
County HIV outbreak. Data to be analyzed include: (1) emergency room (ER) admissions and discharges
related to opioid use and soft tissue infections related to drug abuse; (2) HIV testing surveillance data; (3) HCV
incidence data; (4) search engine inquires of relevant topics, such as Google or Bing searches for “HIV testing”
and “Opana”; (5) law enforcement arrest records (particularly those related to opioid possession and
distribution); and (6) electronic healthcare reimbursement data of HIV-related treatments (e.g., post-exposure
prophylaxis). Automated data/text mining and machine learning techniques will also be applied to (7) social
media data (i.e., Twitter tweets and Reddit forum posts) that make reference to HIV, Opana, substance use,
and other terms to determine if trends in social media data could have predicted HIV's arrival in, and
transmission throughout, Scott County. Using the diverse data sources listed above, our team will correlate
the time series of key predictor variables to identify the data source (or sources) most predictive of a known
HIV outbreak. If our team develops a health informatics approach and algorithm(s) identifying trends in social
media and other electronic data indicating an imminent HIV outbreak, state and county health departments can
use these “signals” to increase the number of HIV testing and counseling sites in the affected area, health care
providers can more aggressively screen for HIV/STI infection, syringe-service programs can be mobilized
rapidly and targeted more efficiently, contact tracing activities can be initiated, and PEP and PrEP can be
prescribed to those at risk for HIV infection in the geographic region of concern. This study will also examine
feasibility issues related to the collection and analysis of ...

## Key facts

- **NIH application ID:** 10017931
- **Project number:** 5R21DA047893-02
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** Timothy Glenn Heckman
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $198,078
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017931

## Citation

> US National Institutes of Health, RePORTER application 10017931, Health Informatics to Model the Scott County HIV Outbreak (5R21DA047893-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10017931. Licensed CC0.

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