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.
[1] 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
[2] WHO. 2002. Innovative Care for Chronic Conditions Building Block for
Action. www.who.int/chp/knowledge/publications/icccglobalreport.pdf
(2007)
[3] WHO. 2010. Regional Office for South-East Asia Region.
http://www.searo.who.int/index.htm .
(2010)
[4] 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.
[5] Cox, N. J. & Subbarao, K. 2000. Global epidemiology of influenza: past
and present. Annual Review of Medicine 51(1): 407-421.
[6] Potter, C. W. 2001. A history of influenza. Journal of applied
microbiology 91(4): 572-579.
[7] 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).
[8] Sarubbi, F. A. 2003. Influenza: A Historical Perspective. Southern
Medical Journal 96(8): 735
[9] Rahmat, R.B.H. 2004. Ministry of Health Malaysia Influenza System
(M.I.S.S) Clinical & Laboratory Surveillance. Ministry of Health
Malaysia
[10] CDC. 2007. Key facts about influenza and influenza vaccine.
www.cdc.gov/flu/keyfacts.htm. (Sept 2010).
[11] 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.
[12] Zimmer, S. M. & Burke, D. S. 2009. Historical perspective--Emergence
of influenza A (H1N1) viruses. The New England journal of medicine
361(3): 279.
[13] 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).
[14] 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.
[15] Connolly, M. A. 2005. Communicable disease control in emergencies: a
field manual. Geneva: WHO
[16] 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.
[17] 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)
[18] MDH, Minnesota Department of Health .2007. "Food Borne Outbreak".
http://www.health.state.mn.us/ .(20 July 2009).
[19] ICPH, Island Country Public Health. 2009. " Outbreak Investigation
Procedure Island County Public Health Personnel”.
http://www.islandcounty.net/health/outbreak.htm. (Jan 2010).
[20] 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).
[21] CEE, The Columbia Electronic Encyclopedia . 2004. "Influenza
Outbreak". Columbia University Press.
http://www.questia.com/library/encyclopedia/columbia_university.jsp.
(July 2009).
[22] Dictionary, A. H. 2000. American heritage dictionary. The American
Heritage Dictionary of the English Language
[23] 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)
[24] Seng, S. B., Chong, A. K. & Moore, A. 2005. Geostatistical modelling,
analysis and mapping of epidemiology of Dengue fever in Johor State,
Malaysia.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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
[30] 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.
[31] Hodge, V. & Austin, J. 2004. A Survey of Outlier Detection
Methodologies. Artificial Intelligence Review 22(2): 85-126.
[32] Patcha, A. & Park, J. M. 2007. An overview of anomaly detection
techniques: Existing solutions and latest technological trends. Computer
Networks 51(12): 3448-3470.
[33] Zhang, Y., Meratnia, N. & Havinga, P. J. M. 2007. A taxonomy
framework for unsupervised outlier detection techniques for multi-type
data sets.
[34] 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.
[35] Chandola, V., Banerjee, A. & Kumar, V. 2009. Anomaly detection: A
survey. ACM Computing Surveys (CSUR) 41(3): 15.
[36] 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.
[37] Zhang, J. 2008. Towards outlier detection for high-dimensional data
streams using projected outlier analysis strategy. Tesis Ph.D. Dalhousie
University.
[38] 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.
[39] 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.
[40] 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.
[41] 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.
[42] 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.
[43] 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.
[44] 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.
[45] Zalizah A. L, A. R. H., Azuraliza A.B. 2010. Multiple Attribute
Frequent Mining-based for Dengue Outbreak. Lecture Notes In
Computer Science.
[1] 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
[2] WHO. 2002. Innovative Care for Chronic Conditions Building Block for
Action. www.who.int/chp/knowledge/publications/icccglobalreport.pdf
(2007)
[3] WHO. 2010. Regional Office for South-East Asia Region.
http://www.searo.who.int/index.htm .
(2010)
[4] 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.
[5] Cox, N. J. & Subbarao, K. 2000. Global epidemiology of influenza: past
and present. Annual Review of Medicine 51(1): 407-421.
[6] Potter, C. W. 2001. A history of influenza. Journal of applied
microbiology 91(4): 572-579.
[7] 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).
[8] Sarubbi, F. A. 2003. Influenza: A Historical Perspective. Southern
Medical Journal 96(8): 735
[9] Rahmat, R.B.H. 2004. Ministry of Health Malaysia Influenza System
(M.I.S.S) Clinical & Laboratory Surveillance. Ministry of Health
Malaysia
[10] CDC. 2007. Key facts about influenza and influenza vaccine.
www.cdc.gov/flu/keyfacts.htm. (Sept 2010).
[11] 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.
[12] Zimmer, S. M. & Burke, D. S. 2009. Historical perspective--Emergence
of influenza A (H1N1) viruses. The New England journal of medicine
361(3): 279.
[13] 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).
[14] 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.
[15] Connolly, M. A. 2005. Communicable disease control in emergencies: a
field manual. Geneva: WHO
[16] 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.
[17] 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)
[18] MDH, Minnesota Department of Health .2007. "Food Borne Outbreak".
http://www.health.state.mn.us/ .(20 July 2009).
[19] ICPH, Island Country Public Health. 2009. " Outbreak Investigation
Procedure Island County Public Health Personnel”.
http://www.islandcounty.net/health/outbreak.htm. (Jan 2010).
[20] 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).
[21] CEE, The Columbia Electronic Encyclopedia . 2004. "Influenza
Outbreak". Columbia University Press.
http://www.questia.com/library/encyclopedia/columbia_university.jsp.
(July 2009).
[22] Dictionary, A. H. 2000. American heritage dictionary. The American
Heritage Dictionary of the English Language
[23] 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)
[24] Seng, S. B., Chong, A. K. & Moore, A. 2005. Geostatistical modelling,
analysis and mapping of epidemiology of Dengue fever in Johor State,
Malaysia.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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
[30] 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.
[31] Hodge, V. & Austin, J. 2004. A Survey of Outlier Detection
Methodologies. Artificial Intelligence Review 22(2): 85-126.
[32] Patcha, A. & Park, J. M. 2007. An overview of anomaly detection
techniques: Existing solutions and latest technological trends. Computer
Networks 51(12): 3448-3470.
[33] Zhang, Y., Meratnia, N. & Havinga, P. J. M. 2007. A taxonomy
framework for unsupervised outlier detection techniques for multi-type
data sets.
[34] 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.
[35] Chandola, V., Banerjee, A. & Kumar, V. 2009. Anomaly detection: A
survey. ACM Computing Surveys (CSUR) 41(3): 15.
[36] 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.
[37] Zhang, J. 2008. Towards outlier detection for high-dimensional data
streams using projected outlier analysis strategy. Tesis Ph.D. Dalhousie
University.
[38] 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.
[39] 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.
[40] 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.
[41] 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.
[42] 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.
[43] 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.
[44] 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.
[45] Zalizah A. L, A. R. H., Azuraliza A.B. 2010. Multiple Attribute
Frequent Mining-based for Dengue Outbreak. Lecture Notes In
Computer Science.
@article{"International Journal of Medical, Medicine and Health Sciences:49241", author = "Zalizah Awang Long and Abdul Razak Hamdan and Azuraliza Abu Bakar", title = "Anomaly Based On Frequent-Outlier for Outbreak Detection in Public Health Surveillance", abstract = "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.
", keywords = "Outlier detection, frequent-outlier, outbreak, anomaly, surveillance, public health", volume = "7", number = "3", pages = "125-8", }
