Outbreak detection model based on danger theory

In outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance betwee...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak
Format: Article
Published: Elsevier B.V. 2014
Subjects:
Online Access:http://repo.uum.edu.my/14124/
http://doi.org/10.1016/j.asoc.2014.08.030
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Utara Malaysia
id my.uum.repo.14124
record_format eprints
spelling my.uum.repo.141242016-05-22T07:21:03Z http://repo.uum.edu.my/14124/ Outbreak detection model based on danger theory Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak QA76 Computer software In outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance between high detection rate and low false alarm rate occurs. To solve this problem, this study proposes a novel outbreak detection model based on danger theory; a bio-inspired method that replicates how the human body fights pathogens. We propose a signal formalization approach based on cumulative sum and a cumulative mature antigen contact value to suit the outbreak characteristic and danger theory. Two outbreak diseases, dengue and SARS, are subjected to a danger theory algorithm; namely the dendritic cell algorithm.To evaluate the model, four measurement metrics are applied: detection rate, specificity, false alarm rate, and accuracy. From the experiment, the proposed model outperforms the other detection approaches and shows a significant improvement for both diseases outbreak detection. The findings reveal that the robustness of the proposed immune model increases when dealing with inconsistent outbreak signals. The model is able to detect new unknown outbreak patterns and can discriminate between outbreak and non-outbreak cases with a consistent high detection rate, high sensitivity, and lower false alarm rate even without a training phase. Elsevier B.V. 2014 Article PeerReviewed Mohamad Mohsin, Mohamad Farhan and Abu Bakar, Azuraliza and Hamdan, Abdul Razak (2014) Outbreak detection model based on danger theory. Applied Soft Computing, 24. pp. 612-622. ISSN 1568-4946 http://doi.org/10.1016/j.asoc.2014.08.030 doi:10.1016/j.asoc.2014.08.030
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
topic QA76 Computer software
spellingShingle QA76 Computer software
Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Azuraliza
Hamdan, Abdul Razak
Outbreak detection model based on danger theory
description In outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance between high detection rate and low false alarm rate occurs. To solve this problem, this study proposes a novel outbreak detection model based on danger theory; a bio-inspired method that replicates how the human body fights pathogens. We propose a signal formalization approach based on cumulative sum and a cumulative mature antigen contact value to suit the outbreak characteristic and danger theory. Two outbreak diseases, dengue and SARS, are subjected to a danger theory algorithm; namely the dendritic cell algorithm.To evaluate the model, four measurement metrics are applied: detection rate, specificity, false alarm rate, and accuracy. From the experiment, the proposed model outperforms the other detection approaches and shows a significant improvement for both diseases outbreak detection. The findings reveal that the robustness of the proposed immune model increases when dealing with inconsistent outbreak signals. The model is able to detect new unknown outbreak patterns and can discriminate between outbreak and non-outbreak cases with a consistent high detection rate, high sensitivity, and lower false alarm rate even without a training phase.
format Article
author Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Azuraliza
Hamdan, Abdul Razak
author_facet Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Azuraliza
Hamdan, Abdul Razak
author_sort Mohamad Mohsin, Mohamad Farhan
title Outbreak detection model based on danger theory
title_short Outbreak detection model based on danger theory
title_full Outbreak detection model based on danger theory
title_fullStr Outbreak detection model based on danger theory
title_full_unstemmed Outbreak detection model based on danger theory
title_sort outbreak detection model based on danger theory
publisher Elsevier B.V.
publishDate 2014
url http://repo.uum.edu.my/14124/
http://doi.org/10.1016/j.asoc.2014.08.030
_version_ 1644281368017895424