ArticleJournal 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 ban2017
'Outbreak Gold Standard' selection to provide optimized threshold for infectious diseases early-alert based on China Infectious Disease Automated-alert and Response System.
Article in 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, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A framework using large time series model for early warning of infectious diseases.Infectious Disease Modelling · 2026Article
- The early warning and response systems in Syria: A functionality and alert threshold assessment.IJID regions · 2025Article
- Integrated health surveillance and early warning systems in China under the One Health perspective: progress and challenges.Science in One Health · 2025Review
- Differences in the Clinical Severity Scores of Hospitalized COVID-19 Patients across Different Phases of an Epidemic Wave in Iran: a Secondary Analysis.Medical journal of the Islamic Republic of Iran · 2024Article
- A H9N2 Human Case and Surveillance of Avian Influenza Viruses in Live Poultry Markets - Huizhou City, Guangdong Province, China, 2021.China CDC weekly · 2022Article
- Analysis of Monitoring, Early Warning and Emergency Response System for New Major Infectious Diseases in China and Overseas.Current medical science · 2021Review
- Strengthened Monitoring of H5 Avian Influenza Viruses in External Environment in Hubei, 2018.Current medical science · 2020Article
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Authors and funding
5 authors.
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Abstract
The China Infectious Disease Automated-alert and Response System (CIDARS) was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control (CDC) at all levels in China. In the CIDARS, thresholds are determined using the "Mean+2SD‟ in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the "Mean +2SD‟ method to the performance of 5 novel algorithms to select optimal "Outbreak Gold Standard (OGS)‟ and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The "Mean+2SD‟, C1, C2, moving average (MA), seasonal model (SM), and cumulative sum (CUSUM) algorithms were applied. Outbreak signals for the predicted value (Px) were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A (chickenpox and mumps), TYPE B (influenza and rubella) and TYPE C [hand foot and mouth disease (HFMD) and scarlet fever]. Optimized thresholds for chickenpox (P
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