166 - HOW PEOPLE WITH TYPE 1 DIABETES CAN ASSIST IN DISEASE OUTBREAK DETECTION
Abstract
Background and Aims
After 9/11 and several global epidemics, syndromic surveillance has become an even more important part of a country’s national emergency preparedness. The challenge is to detect the spread of an infectious disease as early as possible. This project is based on a hypothesis that through analysis of health data from people with Type 1 Diabetes (T1D), it is possible to detect a disease close to the point of infection, i.e., long before the disease onset.
Methods
An in-house Electronic Disease Surveillance Network, EDMON system, has been developed to test our hypothesis. EDMON is a real-time early disease outbreak detection system that uses self-recorded health data from people with T1D. The models have been tested using 11 years of high precision continues data recorded from 3 individuals with T1D.
Results
By combining continuously recorded health data (blood glucose, insulin, carbohydrates, physical activity and geographical location) from people with T1D, we are able to detect abnormal changes in their BG, changes that indicate that they have been infected with a pathogen. The discrepancies are identified by the use of advanced statistical and machine learning models.
Conclusions
Through the EDMON system, we can receive real-time information about the spread of infectious diseases in our neighborhood, and thus, can take the necessary action in order to reduce the risk of becoming infected. EDMON might be a very useful tool for people with T1D as well as the rest of the population in disease prevention.
