ArticleBMC public health2012
Epidemic features affecting the performance of outbreak detection algorithms.
Article in BMC public health, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review.JMIR public health and surveillance · 2021Pooled it
- Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017.Statistics in medicine · 2020Article
- Establishing seasonal and alert influenza thresholds in Morocco.BMC public health · 2020Article
- Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System.The Journal of international medical research · 2018Article
- 'Outbreak Gold Standard' selection to provide optimized threshold for infectious diseases early-alert based on China Infectious Disease Automated-alert and Response System.Journal of Huazhong University of Science and Technology. Medical sciences = Hua zhong ke ji da xue xue bao. Yi xue Ying De wen ban = Huazhong keji daxue xuebao. Yixue Yingdewen ban · 2017Article
- How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS).BMC public health · 2017Article
- Hand, foot and mouth disease in China: evaluating an automated system for the detection of outbreaks.Bulletin of the World Health Organization · 2014Article
- Exploring a proposed WHO method to determine thresholds for seasonal influenza surveillance.PloS one · 2013Article
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5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOutbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms.
methodsExponentially weighted moving average (EWMA), cumulative sum (CUSUM) and moving percentile method (MPM) algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS), and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms' sensitivity and timeliness with the epidemic features of infectious diseases.
resultsThe MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude). The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (β* = -0.13, P < 0.001) but needed shorter time to detect outbreaks (β* = -0.57, P < 0.001). Lower baseline counts were associated with higher probability (β* = -0.20, P < 0.001) and longer time (β* = 0.14, P < 0.001). The larger outbreak magnitude was correlated with higher probability (β* = 0.55, P < 0.001) and shorter time (β* = -0.23, P < 0.001).
conclusionsThe results of this study suggest that the MPM is a prior algorithm for outbreak detection and differences of epidemic features in detection performance should be considered in automatic surveillance practice.
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