Volume 9, Issue 2 (Vol 9, No 2, Summer 2013 2013)
irje 2013, 9(2): 29-38 |
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Karami M, Soori H, Mehrabi Y, Haghdoost A, Gouya M, Esmailnasab N. Evaluating the Performance of an Outbreak- Detection Algorithms using Semi-Synthetic Approach: Cumulative Sum Algorithm. irje 2013; 9 (2) :29-38
URL: http://irje.tums.ac.ir/article-1-5065-en.html
URL: http://irje.tums.ac.ir/article-1-5065-en.html
1- Assistant Professor of Epidemiology, Research Center for Modeling of Noncommunicable Diseasesand Department of Biostatistics & Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran , ma.karami@umsha.ac.ir
2- Professor of Epidemiology, Safety Promotion and Injury Prevention Research Center, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3- Professor of Biostatistics, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- Professor of Epidemiology and Biostatistics, Research Centre of Modeling in Health, Institute of futures studies in health, Kerman University of Medical Sciences, Kerman, Iran
5- Director General, Center for Disease Control, Ministry of Health & Medical Education, Tehran, Iran
6- Associate Professor of Epidemiology, Department of Epidemiology and Biostatistics, Kurdistan University of Medical Sciences, Kurdistan, Iran
2- Professor of Epidemiology, Safety Promotion and Injury Prevention Research Center, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3- Professor of Biostatistics, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- Professor of Epidemiology and Biostatistics, Research Centre of Modeling in Health, Institute of futures studies in health, Kerman University of Medical Sciences, Kerman, Iran
5- Director General, Center for Disease Control, Ministry of Health & Medical Education, Tehran, Iran
6- Associate Professor of Epidemiology, Department of Epidemiology and Biostatistics, Kurdistan University of Medical Sciences, Kurdistan, Iran
Abstract: (15060 Views)
Background & Objectives: Timely response to emerging diseases and outbreaks are a major public health and health systems priority. There are few published studies that evaluate the performance of cumulative sum (CUSUM) on identical data using semi- synthetic simulation approach. This study was undertaken to determine the performance of the CUSUM in timely detection of 831 days of simulated outbreaks.
Methods: We evaluated the performances of the CUSUM as an outbreak detection method on simulated outbreaks injected to daily counts of suspected cases of measles as baseline data in Iran between 21 March 2008 till 20 March 2011. Data obtained from the Iranian national surveillance system. The performance of algorithms was evaluated using sensitivity, false alarm rate, likelihood ratios and Area under the Receiver Operating Characteristic (ROC) curve.
Results: Generally the sensitivity of the CUSUM algorithm in detecting simulated outbreaks was 50% (95% CI: 47- 54). The corresponding values are disaggregated according to outbreak size, shape and duration. The CUSUM algorithm detected the half of outbreaks after 13.84 days on average.
Conclusion: We concluded that CUSUM algorithm performed good in detection of large outbreaks with short periods and poorly in detecting long period outbreaks, particularly those simulated outbreaks that did not begin with a surge of cases.
Methods: We evaluated the performances of the CUSUM as an outbreak detection method on simulated outbreaks injected to daily counts of suspected cases of measles as baseline data in Iran between 21 March 2008 till 20 March 2011. Data obtained from the Iranian national surveillance system. The performance of algorithms was evaluated using sensitivity, false alarm rate, likelihood ratios and Area under the Receiver Operating Characteristic (ROC) curve.
Results: Generally the sensitivity of the CUSUM algorithm in detecting simulated outbreaks was 50% (95% CI: 47- 54). The corresponding values are disaggregated according to outbreak size, shape and duration. The CUSUM algorithm detected the half of outbreaks after 13.84 days on average.
Conclusion: We concluded that CUSUM algorithm performed good in detection of large outbreaks with short periods and poorly in detecting long period outbreaks, particularly those simulated outbreaks that did not begin with a surge of cases.
Keywords: Surveillance System, Measles, Outbreak Detection Method, CUSUM, Semi-synthetic Simulation, Iran
Type of Study: Research |
Subject:
General
Received: 2012/06/26 | Accepted: 2013/01/26 | Published: 2013/10/24
Received: 2012/06/26 | Accepted: 2013/01/26 | Published: 2013/10/24
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