Anomaly Based On Frequent-Outlier for Outbreak Detection in Public Health Surveillance
Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.
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 WHO. 2011. Global Status Report on non-communicable disease 2010. ISBN 978 92 4 068645 8 (online version) www.bmj.com/content/343/bmj.d4888.extract
 WHO. 2002. Innovative Care for Chronic Conditions Building Block for Action. www.who.int/chp/knowledge/publications/icccglobalreport.pdf (2007)
 WHO. 2010. Regional Office for South-East Asia Region. http://www.searo.who.int/index.htm . (2010)
 Setel, P. W., Saker, L., Unwin, N. C., Hemed, Y., Whiting, D. R. & Kitange, H. 2004. Is it time to reassess the categorization of disease burdens in low-income countries? American journal of public health 94(3): 384.
 Cox, N. J. & Subbarao, K. 2000. Global epidemiology of influenza: past and present. Annual Review of Medicine 51(1): 407-421.
 Potter, C. W. 2001. A history of influenza. Journal of applied microbiology 91(4): 572-579.
 Chan, P. K. S. 2002. Outbreak of avian influenza A (H5N1) virus infection in Hong Kong in 1997. Journal Clinical infectious diseases 34(S58-S64).
 Sarubbi, F. A. 2003. Influenza: A Historical Perspective. Southern Medical Journal 96(8): 735
 Rahmat, R.B.H. 2004. Ministry of Health Malaysia Influenza System (M.I.S.S) Clinical & Laboratory Surveillance. Ministry of Health Malaysia
 CDC. 2007. Key facts about influenza and influenza vaccine. www.cdc.gov/flu/keyfacts.htm. (Sept 2010).
 Lau, E. H. Y., Cowling, B. J., Ho, L. M. & Leung, G. M. 2008. Optimizing Use of Multistream Influenza Sentinel Surveillance Data. Emerg Infect Dis 14(7): 1154-7.
 Zimmer, S. M. & Burke, D. S. 2009. Historical perspective--Emergence of influenza A (H1N1) viruses. The New England journal of medicine 361(3): 279.
 German, R. R., Armstrong, G., Birkhead, G. S., Horan, J. M. & Herrera, G. 2001. Updated guidelines for evaluating public health surveillance systems. MMWR Recomm Rep 50(1-35).
 Buehler, J. W., Berkelman, R. L., Hartley, D. M. & Peters, C. J. 2003. Syndromic surveillance and bioterrorism-related epidemics. Emerging Infectious Diseases 9(10): 1197-1204.
 Connolly, M. A. 2005. Communicable disease control in emergencies: a field manual. Geneva: WHO
 Pavlin, J. A., F. Mostashari, et al. (2003). Innovative Surveillance Methods for Rapid Detection of Disease Outbreaks and Bioterrorism: Results of an Interagency Workshop on Health Indicator Surveillance, Am Public Health Assoc. 93: 1230-1235.
 Axcite.2004. "Introduction to Investigating an Outbreak”. Excellence in Curriculum Innovation through Teaching Epidemiology and the Science of Public Health. http://www.cdc.gov/excite/classroom/outbreak/objectives.htm. (May 2008)
 MDH, Minnesota Department of Health .2007. "Food Borne Outbreak". http://www.health.state.mn.us/ .(20 July 2009).
 ICPH, Island Country Public Health. 2009. " Outbreak Investigation Procedure Island County Public Health Personnel”. http://www.islandcounty.net/health/outbreak.htm. (Jan 2010).
 Global Alert and Respond. 2009. " What is the pandemic (H1N1) 2009 virus?” http://www.who.int/csr/disease/swineflu/frequently_asked_questions/ab out_disease/en/index.html (16 April 2010).
 CEE, The Columbia Electronic Encyclopedia . 2004. "Influenza Outbreak". Columbia University Press. http://www.questia.com/library/encyclopedia/columbia_university.jsp. (July 2009).
 Dictionary, A. H. 2000. American heritage dictionary. The American Heritage Dictionary of the English Language
 NZPHA, Public Health Surveillance Information for New Zealand Public Health Action. 2011. "What is Public Health Surveillance”. http://www.surv.esr.cri.nz/ . ( 11 Jan 2011)
 Seng, S. B., Chong, A. K. & Moore, A. 2005. Geostatistical modelling, analysis and mapping of epidemiology of Dengue fever in Johor State, Malaysia.
 Ooi, E. E., Gubler, D. J. & Nam, V. S. 2007. Dengue research needs related to surveillance and emergency response. Report of the Scientific Working Group Meeting on Dengue. Geneva: World Health Organization. hlm. 124-33.
 Runge-Ranzinger, S., Horstick, O., Marx, M. & Kroeger, A. 2008. What does dengue disease surveillance contribute to predicting and detecting outbreaks and describing trends? Tropical Medicine & International Health 13(8): 1022-1041.
 Wagner, M. M., Tsui, F. C., Espino, J. U., Dato, V. M., Sittig, D. F., Caruana, R. A., McGinnis, L. F., Deerfield, D. W., Druzdzel, M. J. & Fridsma, D. B. 2001. The emerging science of very early detection of disease outbreaks. J Public Health Manag Pract 7(6): 51-9.
 Wagner, M. M., Robinson, J. M., Tsui, F. C., Espino, J. U. & Hogan, W. R. 2003. Design of a national retail data monitor for public health surveillance. Journal of the American Medical Informatics Association 10(5): 409-418.
 Wagner, M. M., Espino, J., Tsui, F. C., Gesteland, P., Chapman, W., Ivanov, O., Moore, A., Wong, W., Dowling, J. & Hutman, J. 2004. Syndrome and Outbreak Detection Using Chief-Complaint Data— Experience of the Real-Time Outbreak and Disease Surveillance Project. Syndromic Surveillance
 Widdowson, M. A., Bosman, A., van Straten, E., Tinga, M., Chaves, S., van Eerden, L. & van Pelt, W. 2003. Automated, laboratory-based system using the Internet for disease outbreak detection, the Netherlands. Emerg Infect Dis 9(9): 1046-1052.
 Hodge, V. & Austin, J. 2004. A Survey of Outlier Detection Methodologies. Artificial Intelligence Review 22(2): 85-126.
 Patcha, A. & Park, J. M. 2007. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks 51(12): 3448-3470.
 Zhang, Y., Meratnia, N. & Havinga, P. J. M. 2007. A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets.
 Chandola, V., Mithal, V. & Kumar, V. 2008. A Comparative Evaluation of Anomaly Detection Techniques for Sequence Data. 2008 IEEE International Conference on Data Mining (ICDM). hlm.
 Chandola, V., Banerjee, A. & Kumar, V. 2009. Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3): 15.
 Faizah, Shaari. 2008. Outlier Detection Method Based on Non-Reduct Computation using Rough Sets Theory. Tesis Dr. Falsafah, Fakulti Teknologi dan Sistem Maklumat, Universiti Kebangsaan Malaysia.
 Zhang, J. 2008. Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy. Tesis Ph.D. Dalhousie University.
 Ben-Gal, I. 2005. Outlier Detection. Dlm Maimon. O & Rockach. H. (pnyt). Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Tel Aviv, Israel. Kluwer Academic Publishers.
 Das, K. & Schneider, J. 2007. Detecting anomalous records in categorical datasets. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD. hlm. 220-229.
 Das, K., Schneider, J. & Neill, D. B. 2008. Anomaly pattern detection in categorical datasets. Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM . hlm. 169- 176.
 Das, K., Schneider, J. & Neill, D. B. 2009. Detecting Anomalous Groups in Categorical Datasets. Submitted to the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. hlm.
 He, Z., Xu, X., Huang, Z. & Deng, S. 2005. FP-outlier: Frequent pattern based outlier detection. Computer Science and Information Systems/ComSIS 2(1): 103-118.
 Zalizah A.L, A.R.H, Azuraliza A.B 2011. Frequent Pattern using Multiple Attribute Value for Frequent Itemset Generation. IEEE, Data Mining & Optimization (DMO), UKM.
 Koufakou, A., Ortiz, E., Georgiopoulos, M., Anagnostopoulos, G. & Reynolds, K. 2007. A Scalable and Efficient Outlier Detection Strategy for Categorical Data. IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Patras, Greece. hlm.
 Zalizah A. L, A. R. H., Azuraliza A.B. 2010. Multiple Attribute Frequent Mining-based for Dengue Outbreak. Lecture Notes In Computer Science.