Negative selection algorithm for dengue outbreak detection

Dengue is a critical communicable and vector-borne disease and is becoming a serious concern in Malaysia. It is important to have an early detection system that could provide immediate action, such as the control of dengue transmission at a specific location. However, the available strategy and action may give long-term effects to the community since inaccurate decision making or prediction may lead to other circumstances. Moreover, the need to have a system that can detect the outbreak in a reasonable amount of time is critical. In this study, a nature-inspired computing technique, the artificial immune system (AIS), is used for dengue outbreak detection. One of the variants of the AIS algorithms, called the negative selection algorithm (NSA), has been widely applied in anomaly detection and fault detection. This study aims to employ the NSA for dengue outbreak detection.

Negative selection algorithm for dengue outbreak detection

Dengue is a critical communicable and vector-borne disease and is becoming a serious concern in Malaysia. It is important to have an early detection system that could provide immediate action, such as the control of dengue transmission at a specific location. However, the available strategy and action may give long-term effects to the community since inaccurate decision making or prediction may lead to other circumstances. Moreover, the need to have a system that can detect the outbreak in a reasonable amount of time is critical. In this study, a nature-inspired computing technique, the artificial immune system (AIS), is used for dengue outbreak detection. One of the variants of the AIS algorithms, called the negative selection algorithm (NSA), has been widely applied in anomaly detection and fault detection. This study aims to employ the NSA for dengue outbreak detection.

___

  • T. Faisal, F. Ibrahim, M.N. Taib, “A noninvasive intelligent approach for predicting the risk in dengue patients”, Expert Systems with Applications, Vol. 37, pp. 2175–2181, 2010.
  • WHO, “Dengue and dengue haemorrhagic fevers”, WHO Fact Sheet 117, Geneva, WHO, available at http://www.who.int/mediacentre/factsheets/fs117/en/index.html, 2012.
  • L. Hakim, “Tropical diseases in Malaysia: situational analysis”, Malaysian Ministry of Health, available at www.akademisains.gov.my/download/tropical/Lokman.pdf, 2010.
  • A.A. Bakar, Z. Kefli, S. Abdullah, M. Sahani, “Predictive models for dengue outbreak using multiple rulebase classifiers”, 3rd International Conference on Electrical Engineering and Informatics, pp. 1–6, 2011.
  • M.B. Abdul Hamid, T.K. Abdul Rahman, “Short term load forecasting using an artificial neural network trained by artificial immune system learning algorithm”, 12th International Conference on Computer Modelling and Simulation, pp. 408–413, 2010.
  • L.N. De Castro, J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Berlin, Springer Verlag, 2002.
  • A. Er, M. Rosli, A. Asmahani, M. Mohamad Naim, M. Harsuzilawati, “Spatial mapping of dengue incidence: a case study in Hulu Langat District, Selangor, Malaysia”, International Journal of Human and Social Science, Vol. 15, pp. 410–414, 2010.
  • J. Gubler, “Epidemic dengue/dengue haemorrhagic fever: a global public health problem in the 21st century”, Dengue Bulletin, Vol. 21, pp. 1–120, 1997.
  • N.A. Husin, “Back propagation neural network and non-linear regression models for dengue outbreak prediction”, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System, 2008.
  • R. Muto, “Summary of dengue situation in WHO Western Pacific Region”, Dengue Bulletin, Vol. 22, pp. 12–19, 19 S. Poovaneswari, “Dengue situation in Malaysia”, Malaysian Journal of Pathology, Vol. 15, pp. 3–7, 1993.
  • A.K. Teng, S. Singh, “Epidemiology and new initiatives in the prevention and control of dengue in Malaysia”, Dengue Bulletin, Vol. 25, pp. 7–14, 2001.
  • A.A. Bakar, N. Idris, A.R. Hamdan, Z. Othman, M.Z.A. Nazari, S. Zainudin, “Classification models for outbreak detection in oil and gas pollution area”, 3rd International Conference on Electrical Engineering and Informatics, pp. 1–6, 2011.
  • D.L. Buckeridge, H. Burkom, M. Campbell, W.R. Hogan, A.W. Moore, “Algorithms for rapid outbreak detection: a research synthesis”, Journal of Biomedical Informatics, Vol. 38, pp. 99–113, 2005.
  • Y. Shen, G.F. Cooper, “Multivariate Bayesian modeling of known and unknown causes of events – an application to biosurveillance”, Computer Methods and Programs in Biomedicine, Vol. 107, pp. 436–446, 2010.
  • Z. Long, A.A. Bakar, A.R. Hamdan, M. Sahani, “Multiple attribute frequent mining-based for dengue outbreak”, Advanced Data Mining and Applications, Vol. 6440, pp. 489–496, 2010.
  • G. Shmueli, “Current and potential statistical methods for anomaly detection in modern time series data: the case of biosurveillance”, Data Mining Methods for Anomaly Detection, p. 75, 2005.
  • R. Watkins, S. Eagleson, B. Veenendaal, G. Wright, A. Plant, “Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia”, BMC Medical Informatics and Decision Making, Vol. 8, p. 37, 2008.
  • D. Dasgupta, “Advances in artificial immune systems”, IEEE Computational Intelligence Magazine, Vol. 1, pp. 40–49, 2006.
  • U. Aickelin, “Artificial immune systems (AIS) - a new paradigm for heuristic decision making”, Computing Research Repository, 2008.
  • S. Forrest, A.S. Perelson, L. Allen, R. Cherukuri, “Self-nonself discrimination in a computer”, Research in Security and Privacy, IEEE Computer Society Symposium, pp. 202–212, 1994.
  • E. Bendiab, M.K. Kholladi, “The negative selection algorithm: a supervised learning approach for skin detection and classification”, International Journal of Computer Science and Network Security, Vol. 10, pp. 86–92, 2010. J.R. Al-Enezi, M.F. Abbod, S. Al-Sharhan, “Advancement in artificial immune systems: a perspective of models, algorithms and applications”, 5th IEEE GCC Conference & Exhibition, pp. 1–6, 2009.
  • F. Gonzalez, D. Dasgupta, R. Kozma, “Combining negative selection and classification techniques for anomaly detection”, Proceedings of the IEEE Congress on Evolutionary Computation, pp. 705–710, 2002.
  • C.A. Laurentys, G. Ronacher, R.M. Palhares, W.M. Caminhas, “Design of an artificial immune system for fault detection: a negative selection approach”, Expert Systems with Applications, Vol. 37, pp. 5507–5513, 2010.
  • X. Yue, D. Wen, H. Ma, J. Zhang, “Fault detection based on real-value negative selection algorithm of artificial immune system”, Proceedings of the International Conference on Intelligent Computing and Cognitive Informatics, pp. 243–246, 2010.
  • S.B. Seng, A.K. Chong, A. Moore, “Geostatistical modelling, analysis and mapping of epidemiology of Dengue Fever in Johor State, Malaysia”, Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre, 2005.
  • Z. Ji, D. Dasgupta, “Revisiting negative selection algorithms”, Evolutionary Computation, Vol. 15, pp. 223–251, 200 Z. Ji, D. Dasgupta, “Real-valued negative selection algorithm with variable-sized detectors”, Genetic and Evolutionary Computation, Part 1, Lecture Notes in Computer Science, Vol. 3102, pp. 287–298, 2004.
  • T. Stibor, P. Mohr, J. Timmis, “Is negative selection appropriate for anomaly detection?”, Genetic and Evolutionary Computation Conference, 2005.
  • C. Li, T. Lim, L. Han, R. Fang, “Rainfall, abundance of Aedes aegypti and dengue infection in Selangor, Malaysia”, The Southeast Asian Journal of Tropical Medicine and Public Health, Vol. 16, pp. 560–568, 1985.
  • H. Delatte, G. Gimonneau, A. Triboire, D. Fontenille, “Influence of temperature on immature development, survival, longevity, fecundity, and gonotrophic cycles of Aedes albopictus, vector of chikungunya and dengue in the Indian Ocean”, Journal of Medical Entomology, Vol. 46, pp. 33–41, 2009.
  • G. Kuno, “Review of the factors modulating dengue transmission”, Epidemiologic reviews, Vol. 17, pp. 321–335, 19 J. Patz, “Global warming would foster spread of dengue fever into some temperate regions”, Science Daily, Available at http://www.sciencedaily.com/releases/1998/03/980310081157.htm, 1998.
Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK