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Data Integration and Predictive Analysis System (IPAS)

Award Information
Agency: Department of Defense
Branch: Defense Health Program
Contract: W81XWH-15-C-0158
Agency Tracking Number: H151-008-0215
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DHP15-008
Solicitation Number: 2015.1
Timeline
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-09-21
Award End Date (Contract End Date): 2016-04-20
Small Business Information
1408 University Drive East, College Station, TX, 77840
DUNS: 555403328
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Madhav Erraguntla
 Senior Research Scientist
 (979) 260-5274
 merraguntla@kbsi.com
Business Contact
 Donielle Mayer
Phone: (979) 260-5274
Email: dmayer@kbsi.com
Research Institution
N/A
Abstract
KBSI proposes to design and develop a Data Integration and Predictive Analysis System (IPAS) for the prediction of incidents of human infectious diseases. IPAS will utilize innovative collection of data from open data sources, veterinary and medical professionals, and public observations, together with data cleaning, harmonization, spatio-temporal pattern extraction, factor analysis, and predictive models to provide comprehensive disease incidence prediction. The project will collect and integrate a comprehensive dataset of previous disease occurrences and potential influencing factors like environmental conditions, regional health status and practices, demographics, ethnicity and cultural practices, veterinary and zoonotic indicators, and vector prevalence. Natural language processing (NLP) will be used to extract disease, syndromic, and zoonotic details from news feeds, public health reports, and medical publications. A smartphone app will be used to collect data from situated public, veterinary, and health officials on veterinary, zoonotic, and human signs and symptoms, and on the prevalence of vectors. Machine learning and predictive-analytics-based models will be developed to predict the probability of occurrence of disease for specific geographical locations and times. Phase I will focus on a select CDC Category A disease.

* Information listed above is at the time of submission. *

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