ArticlePloS one2013
Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- One Health in Practice: Using Integrated Bite Case Management to Increase Detection of Rabid Animals in Tanzania.Frontiers in public health · 2020Article
- '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: Critical Community Size and Spatial Vaccination Strategies.Scientific reports · 2016Article
- Hand, foot and mouth disease in China: evaluating an automated system for the detection of outbreaks.Bulletin of the World Health Organization · 2014Article
- Surveillance-response systems: the key to elimination of tropical diseases.Infectious diseases of poverty · 2014Article
- Article
- Need of surveillance response systems to combat Ebola outbreaks and other emerging infectious diseases in African countries.Infectious diseases of poverty · 2014Article
- Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.PloS one · 2014Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1∶4.3%, C2∶11.9%, C3∶10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions.
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