NDLI: A communicable disease prediction benchmarking platform

Content Provider	IEEE Xplore Digital Library
Author	Yigzaw, K.Y. Bellika, J.G.
Copyright Year	2014
Description	Author affiliation: Univ. of Tromso - The Arctic Univ. of Norway, Tromso, Norway (Yigzaw, K.Y.) \|\| Norway & Norwegian Centre for Integrated Care & Telemedicine, Univ. of Tromso - The Arctic Univ. of Norway, Tromso, Norway (Bellika, J.G.)
Abstract	The paper presents a platform for benchmarking disease prediction algorithms and mathematical models. The platform is applied to compare Bayesian and compartmental disease prediction models using. We used weekly aggregated cases of various diseases collected from a microbiology laboratory that covers northern Norway. The platform enables integration and benchmarking of various disease prediction models. Our benchmark shows that the Bayesian model was better on predicting the number of cases on a weekly basis. Normalized root mean square error (NRMSE) for the Bayesian prediction was within the range 0.072-0.1498 for weekly predictions, 0.171-0.254 for monthly. The compartmental SIR(S) model achieved a NRMSE of 0.133 for the weekly prediction against Influenza A data. Disease prediction models benchmarking platforms can help to improve the status of disease prediction systems, investment and time of development. It can speeds up mathematical modeling through its integrated environment for testing and evaluation.
Sponsorship	IEEE Comput. Soc.
Starting Page	564
Ending Page	568
File Size	512025
Page Count	5
File Format	PDF
ISBN	9781479921317
DOI	10.1109/BHI.2014.6864427
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-01
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Predictive models Mathematical model Influenza Bayes methods Data models Computational modeling
Content Type	Text
Resource Type	Article

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