An Early Infectious Disease Outbreak Detection Mechanism Based on Self-Recorded Data from People with Diabetes.

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.