Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study

Authors

  • Mohamad Farhan Mohamad Mohsin School of Computing, Universiti Utara Malaysia, Malaysia
  • Azuraliza Abu Bakar Faculty of Science & Information Technology, Universiti Kebangsaan Malaysia, Malaysia
  • Abdul Razak Hamdan Faculty of Science & Information Technology, Universiti Kebangsaan Malaysia, Malaysia
  • Mazrura Sahani Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Malaysia
  • Zainudin Mohd Ali Negeri Sembilan State Health Department, Negeri Sembilan, Malaysia

DOI:

https://doi.org/10.32890/jict2023.22.3.4

Keywords:

Aedes, artificial immune system, danger theory, dengue, outbreak

Abstract

Dengue is a virus that is spreading quickly and poses a severe threat in Malaysia. It is essential to have an accurate early detection system
that can trigger prompt response, reducing deaths and morbidity. Nevertheless, uncertainties in the dengue outbreak dataset reduce
the robustness of existing detection models, which require a training phase and thus fail to detect previously unseen outbreak patterns.
Consequently, the model fails to detect newly discovered outbreak patterns. This outcome leads to inaccurate decision-making and delays
in implementing prevention plans. Anomaly detection and other detection-based problems have already been widely implemented with
some success using danger theory (DT), a variation of the artificial immune system and a nature-inspired computer technique. Therefore,
this study employed DT to develop a novel outbreak detection model. A Malaysian dengue profile dataset was used for the experiment.
The results revealed that the proposed DT model performed better than existing methods and significantly improved dengue outbreak
detection. The findings demonstrated that the inclusion of a DT detection mechanism enhanced the dengue outbreak detection
model’s accuracy. Even without a training phase, the proposed model consistently demonstrated high sensitivity, high specificity,
high accuracy, and lower false alarm rate for distinguishing between outbreak and non-outbreak instances.

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Published

24-07-2023

Issue

Vol. 22 No. 3 (2023): Journal of Information and Communication Technology (JICT) Vol.22, No.3, July 2023

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Articles

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How to Cite

Mohamad Mohsin, M. F., Abu Bakar, A., Hamdan, A. R., Sahani, M., & Mohd Ali, Z. (2023). Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study. Journal of Information and Communication Technology, 22(3), 399-419. https://doi.org/10.32890/jict2023.22.3.4

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Harvested 2026-09-06
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Identifiers DOI 10.32890/jict2023.22.3.4 OpenAlex W4386103448 Scopus 85168597535

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