An early infectious disease outbreak detection mechanism based on self-recorded data from people with diabetes

Ashenafi Zebene Woldaregay, Eirik Årsand, Taxiarchis Botsis, Gunnar Hartvigsen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

People with diabetes experience elevated blood glucose (BG) levels at the time of an infection. We propose to utilize patient-gathered information in an Electronic Disease Surveillance Monitoring Network (EDMON), which may support the identification of a cluster of infected people with elevated BG levels on a spatiotemporal basis. The system incorporates data gathered from diabetes apps, continuous glucose monitoring (CGM) devices, and other appropriate physiological indicators from people with type 1 diabetes. This paper presents a novel approach towards modeling of the individual's BG dynamics, a mechanism to track and detect deviations of elevated BG readings. The models were developed and validated using self-recorded data in the non-infection status using Dexcom CGM devices, from two type 1 diabetes individuals over a 1-month period. The models were also tested using simulated datasets, which resemble the individual's BG evolution during infections. The models accurately simulated the individual's normal BG fluctuations and further detected statistically significant BG elevations.

Original languageEnglish (US)
Title of host publicationMEDINFO 2017
Subtitle of host publicationPrecision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics
PublisherIOS Press
Pages619-623
Number of pages5
ISBN (Electronic)9781614998297
DOIs
Publication statusPublished - Jan 1 2017
Externally publishedYes
Event16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China
Duration: Aug 21 2017Aug 25 2017

Publication series

NameStudies in Health Technology and Informatics
Volume245
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
CountryChina
CityHangzhou
Period8/21/178/25/17

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Keywords

  • Blood glucose
  • Diabetes mellitus
  • Disease outbreaks
  • Type 1

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Woldaregay, A. Z., Årsand, E., Botsis, T., & Hartvigsen, G. (2017). An early infectious disease outbreak detection mechanism based on self-recorded data from people with diabetes. In MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics (pp. 619-623). (Studies in Health Technology and Informatics; Vol. 245). IOS Press. https://doi.org/10.3233/978-1-61499-830-3-619