Pattern Mining for Outbreak Discovery Preparedness

Pattern Mining for Outbreak Discovery Preparedness

Zalizah Awang Long (Malaysia Institute Information Technology, Universiti Kuala Lumpur, Malaysia), Abdul Razak Hamdan (Universiti Kebengsaan Malaysia, Malaysia), Azuraliza Abu Bakar (Universiti Kebengsaan Malaysia, Malaysia) and Mazrura Sahani (Universiti Kebengsaan Malaysia, Malaysia)
Copyright: © 2012 |Pages: 13
DOI: 10.4018/978-1-4666-1803-9.ch008
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Abstract

Today, the objective of public health surveillance system is to reduce the impact of outbreaks by enabling appropriate intervention. Commonly used techniques are based on the changes or aberration in health events when compared with normal history to detect an outbreak. The main problem encountered in outbreaks is high rates of false alarm. High false alarm rates can lead to unnecessary interventions, and falsely detected outbreaks will lead to costly investigation. In this chapter, the authors review data mining techniques focusing on frequent and outlier mining to develop generic outbreak detection process model, named as “Frequent-outlier” model. The process model was tested against the real dengue dataset obtained from FSK, UKM, and also tested on the synthetic respiratory dataset obtained from AUTON LAB. The ROC was run to analyze the overall performance of “frequent-outlier” with CUSUM and Moving Average (MA). The results were promising and were evaluated using detection rate, false positive rate, and overall performance. An important outcome of this study is the knowledge rules derived from the notification of the outbreak cases to be used in counter measure assessment for outbreak preparedness.
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Background

There is no specific definition in defining outbreak. According to Lai & Kwong, (2010), from the epidemiology view, an outbreak occurs if individuals develop similar symptoms one after another and the disease incidence is higher than usual. The general definition from Center Disease Control (CDC) defined an outbreak as the occurrence of more cases of disease than what is expected in a given area over a particular period of time. Different diseases possess their own outbreak definition. Dengue outbreak, for example, is defined as increase of cases per week persisting for at least 3 successive weeks to a level at least three times above the mean of previous 3 weeks (Runge-Ranzinger, Horstick, Marx, & Kroeger, 2008). Another definition obtained for dengue outbreaks is an increase in 2 SD above the mean ((Carme, et al., 2003; Oum, Chandramohan, & Cairncross, 2005; Rigau-Pérez, et al., 1998; Talarmin, et al., 2000)). In this study, the focus concentrates on the Malaysia dengue environment. Based on the study conducted by Seng, Chong, & Moore, (2005) the dengue outbreak was defined according to Johor Health State Department, which is an occurrence of more than one case in the same locality, where the date of onset between the cases are greater than 14 days. The outbreak is clear when there is no new case reported within 14 days.

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