Conference article

EDMON - A Wireless Communication Platform for a Real-Time Infectious Disease Outbreak De- tection System Using Self-Recorded Data from People with Type 1 Diabetes

Ashenafi Zebene Woldaregay
Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Eirik Årsand
Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway

Alain Giordanengo
Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway / Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway

David Albers
Columbia University, N.Y., USA

Lena Mamykina
Columbia University, N.Y., USA

Taxiarchis Botsis
Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Gunnar Hartvigsen
Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway / Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway

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Published in: Proceedings from The 15th Scandinavian Conference on Health Informatics 2017 Kristiansand, Norway, August 29–30, 2017

Linköping Electronic Conference Proceedings 145:3, p. 14-20

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Published: 2018-01-04

ISBN: 978-91-7685-364-1

ISSN: 1650-3686 (print), 1650-3740 (online)


The relation between an infection incident and elevated blood glucose (BG) levels has been known for long time. People with diabetes often experience variable episodes of elevated BG levels up on infections incident. Hence, we proposed an Electronic Disease Surveillance Monitoring Network (EDMON) that uses BG pattern and other relevant parameters to detect infected diabetes individuals during the incubation period. The project is an extension of the results achieved in the mobile diabetes (mDiabetes) field within our research team for the last 15 years. The proposed EDMON system is a kind of public health surveillance, which uses events analysis at individual levels (called micro events) to reach on a conclusion for uncovering events on the general populations (called macro events) based on spatio-temporal cluster detection. It incorporates self-management mobile apps, sensors, wearables, and point of care (POC) devices to collect real-time health information from individuals with Type 1 diabetes. In this paper, we will present the proposed EDMON system architecture along with the design requirements, system components, communication protocols and challenges involved herein.


Type 1 Diabetes, Wireless Communication, BG Pattern Detection, Infection Detection


[1] (Idf), I. D. F. 2015, IDF diabetes atlas - 7th edition[Online]. Available:

[2] Walseth, O. A., Arsand, E., Sund, T. & Skipenes, E.2005, Wireless Transfer of Sensor Data intoElectronic Health Records. Connecting MedicalInformatics and Bio-Informatics. IOS Press.

[3] Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W.,Schussler-Fiorenza Rose, S. M., Perelman, D.,Colbert, E., Runge, R., Rego, S., Sonecha, R., Datta,S., Mclaughlin, T. & Snyder, M. P. 2017 DigitalHealth: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information. PLoS Biol, 15, e2001402.

[4] Issom, D.-Z., Woldaregay, A. Z., Chomutare, T.,Bradway, M., Arsand, E. & Hartvigsen, G. 2015,Mobile applications for people with diabetespublished between 2010 and 2015. DiabetesManagement, 5, 539-550.

[5] Botsis, T., Walderhaug, S., Dias, A., Van Vuurden,K., Bellika, J. G. & Hartvigsen, G. 2009, Point-ofcaredevices for healthy consumers - a feasibilitystudy. J Telemed Telecare, 15, 419-420.

[6] Beranger, J. 2016, Introduction. Big Data andEthics. Elsevier, xi-xxxvi.

[7] Mohammadi, D. 2015, Turning big data intopersonalised diabetes care. The Lancet Diabetes &Endocrinology, 3, 935-936.

[8] Vayena, E., Salathe, M., Madoff, L. C. &Brownstein, J. S. 2015, Ethical challenges of bigdata in public health. PLoS Comput Biol, 11,e1003904.

[9] Swan, M. 2013, The Quantified Self: FundamentalDisruption in Big Data Science and BiologicalDiscovery. Big Data, 1, 85-99.

[10] Council, C. S. C. 2017, Impact of Cloud Computingon Healthcare Version 2.0.

[11] Choi, J., Cho, Y., Shim, E. & Woo, H. 2016, Webbasedinfectious disease surveillance systems andpublic health perspectives: a systematic review.BMC Public Health, 16, 1238.

[12] Pivette, M., Mueller, J. E., Crepey, P. & Bar-Hen, A.2014, Drug sales data analysis for outbreakdetection of infectious diseases: a systematicliterature review. BMC Infect Dis, 14, 604.

[13] Lawpoolsri, S., Khamsiriwatchara, A., Liulark, W.,Taweeseneepitch, K., Sangvichean, A.,Thongprarong, W., Kaewkungwal, J. &Singhasivanon, P. 2014, Real-time monitoring ofschool absenteeism to enhance diseasesurveillance: a pilot study of a mobile electronicreporting system. JMIR mHealth uHealth, 2, e22.

[14] Paterson, B., Caddis, R. & Durrheim, D. 2011, Useof workplace absenteeism surveillance data foroutbreak detection. Emerg Infect Dis, 17, 1963-1964.

[15] Holt, J. B., Huston, S. L., Heidari, K., Schwartz, R.,Gollmar, C. W., Tran, A., Bryan, L., Liu, Y. & Croft,J. B. 2015, Indicators for Chronic DiseaseSurveillance — United States, 2013. CDC.

[16] Botsis, T., Lai, A. M., Palmas, W., Starren, J. B.,Hartvigsen, G. & Hripcsak, G. 2012, Proof ofconcept for the role of glycemic control in theearly detection of infections in diabetics. HealthInformatics Journal, 18, 26-35.

[17]T.Botsis, O.Hejlesen, J.G.Bellika & G.Hartvigsen2007, Blood glucose levels as an indicator for theearly detection of infections in type-1 diabetics.Advances in Disease Surveillance.

[18] Botsis, T. & Hartvigsen, G. 2010, Exploring newdirections in disease surveillance for people withdiabetes: lessons learned and future plans. StudHealth Technol Inform, 160, 466-470.

[19] Lauritzen, J. N., Arsand, E., Van Vuurden, K.,Bellika, J. G., Hejlesen, O. K. & Hartvig-Sen, G.2011, Towards a mobile solution for predictingillness in Type 1 Diabetes Mellitus: Developmentof a prediction model for detecting risk of illnessin Type 1 Diabetes prior to symptom onset. 1-5.

[20] 20. Woldaregay, A. Z., Van Vuurden, K.,Arsand, E., Botsis, T. & Hartvigsen, G. ElectronicDisease Surveillance System Based on Input fromPeople with Diabetes: An Early OutbreakDetection Mechanism. Proceedings from The14th Scandinavian Conference on HealthInformatics 2016, Gothenburg, Sweden, April 6-72016, 2016. Linkoping University Electronic Press,23-27.

[21] 21. Arsand, E., Walseth, O., Andersson, N.,Fernando, R., Granberg, O., Bellika, J. &Hartvigsen, G. 2005, Using blood glucose data asan indicator for epidemic disease outbreaks.Studies in Health Technology and Informatics, 116,217-222.

[22] 22. Rayfield, E. J., Ault, M. J., Keusch, G. T.,Brothers, M. J., Nechemias, C. & Smith, H. 1982,Infection and diabetes: the case for glucosecontrol. Am J Med, 72, 439-450.

[23] 23. Beranger, J. 2016, 1 - The Shift towards aConnected, Assessed and Personalized MedicineCentered Upon Medical Datasphere Processing.Big Data and Ethics. Elsevier, 1-95.

[24] 24. Huzooree, G., Khedo, K. K. & Joonas, N.2017, Wireless Body Area Network SystemArchitecture for Real-Time Diabetes Monitoring.In: Fleming, P., Vyas, N., Sanei, S. & Deb, K. (eds.)Emerging Trends in Electrical, Electronic andCommunications Engineering: Proceedings of theFirst International Conference on Electrical,Electronic and Communications Engineering(ELECOM 2016), Bagatelle, Mauritius, November25 -27, 2016. Cham: Springer InternationalPublishing, 262-271.

[25] Liao, Y. T., Tang, S. T., Chen, T. C., Tsao, C. H., Lee,T. C., Huang, Y. F. & Young, S. T. 2004, Acommunication platform for diabetessurveillance. Conf Proc IEEE Eng Med Biol Soc, 5,3313-3315.

[26] Mougiakakou, S. G., Bartsocas, C. S., Bozas, E.,Chaniotakis, N., Iliopoulou, D., Kouris, I.,Pavlopoulos, S., Prountzou, A., Skevofilakas, M.,Tsoukalis, A., Varotsis, K., Vazeou, A., Zarkogianni,K. & Nikita, K. S. 2010, SMARTDIAB: acommunication and information technologyapproach for the intelligent monitoring,management and follow-up of type 1 diabetespatients. IEEE Trans Inf Technol Biomed, 14, 622-633.

[27] Mougiakakou, S., Stoitsis, J., Iliopoulou, D.,Prentza, A., Nikita, K. & Koutsouris, D. 2005, ACommunication Platform for Tele-monitoring and Tele-management of Type 1 Diabetes. Conf ProcIEEE Eng Med Biol Soc, 3, 2207-2210.

[28] Martinez, A., Ruba, W., Sanchez, A. B., Meneu, M.T. & Traver, V. 2011, Architecture for LifestyleMonitoring Platform in Diabetes Management.55, 194-200.

[29] Al-Taee, M. A., Al-Nuaimy, W., Al-Ataby, A.,Muhsin, Z. J. & Abood, S. N. 2015, Mobile healthplatform for diabetes management based on theInternet-of-Things. 1-5.

[30] Chang, S.-H., Chiang, R.-D., Wu, S.-J. & Chang, W.-T. 2016, A Context-Aware, Interactive M-HealthSystem for Diabetics. IT Professional, 18, 14-22.

[31] (Nse), N. C. F. E.-H. R. Diabetes Diary [Online].Available:

[32] Granberg, O., Bellika, J. G., Arsand, E. &Hartvigsen, G. 2007, Automatic infectiondetection system. Stud Health Technol Inform,129, 566-570.

[33] Sachidananda, V., Khelil, A. & Suri, N. 2010,Quality of information in wireless sensornetworks: A survey. ICIQ (to appear).

[34] Zahedi, S., Srivastava, M. B. & Bisdikian, C. 2008, Acomputational framework for quality ofinformation analysis for detection-orientedsensor networks. 1-7.

[35] Rafe, V. & Hajvali, M. 2014, A reliable architecturalstyle for designing pervasive healthcare systems. JMed Syst, 38, 86.

[36] Arsand, E., Bradway, M., Giordanengo, A., Mužny,M. & Wangberg, S. C. 2016, The need to tailormobile phone-based diabetes self-managementtools. The 9th International Conference onAdvanced Technologies & Treatments for Diabetes(ATTD 2016) Milan, Italy.

[37] Office for Civil Rights, O. 2012, GuidanceRegarding Methods for De-identification ofProtected Health Information in Accordance withthe Health Insurance Portability andAccountability Act (HIPAA) Privacy Rule [Online].Available:

[38] Uzuner, O., Luo, Y. & Szolovits, P. 2007, Evaluatingthe state-of-the-art in automatic de-identification.J Am Med Inform Assoc, 14, 550-563.

[39] Niewiadomska-Szynkiewicz, E. 2013, EnergyAware Communication Protocols for WirelessSensor Networks. 7776, 135-149.

[40] Gautam, A. K. & Gautam, A. K. 2009, A Protocol forEnergy Efficient, Location Aware, Uniform andGrid Based Hierarchical Organization of WirelessSensor Networks. 40, 273-283.

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