ArticleBMC public health2017
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).
Article in BMC public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.BMJ paediatrics open · 2026Article
- A framework using large time series model for early warning of infectious diseases.Infectious Disease Modelling · 2026Article
- Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.International journal of environmental research and public health · 2025Article
- Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic.Infectious Disease Modelling · 2025Review
- 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
- Simple methods for early warnings of COVID-19 surges: Lessons learned from 21 months of wastewater and clinical data collection in Detroit, Michigan, United States.The Science of the total environment · 2023Article
- Identifying Hot Spots of Tuberculosis in Nigeria Using an Early Warning Outbreak Recognition System: Retrospective Analysis of Implications for Active Case Finding Interventions.JMIR public health and surveillance · 2023Article
- Quality of National Disease Surveillance Reporting before and during COVID-19: A Mixed-Method Study in Indonesia.International journal of environmental research and public health · 2022Article
- Establishment of Outbreak Thresholds for Hepatitis A in South Africa Using Laboratory Surveillance, 2017-2020.Viruses · 2021Article
- Analysis of Monitoring, Early Warning and Emergency Response System for New Major Infectious Diseases in China and Overseas.Current medical science · 2021Review
- Exploring Vector-Borne Disease Surveillance and Response Systems in Beijing, China: A Qualitative Study from the Health System Perspective.International journal of environmental research and public health · 2020Article
- Earlier detection of public health risks - Health policy lessons for better compliance with the International Health Regulations (IHR 2005): Insights from low-, mid- and high-income countries.Health policy (Amsterdam, Netherlands) · 2019Article
- Dynamic Bayesian network in infectious diseases surveillance: a simulation study.Scientific reports · 2019Article
- Monitoring telehealth vomiting calls as a potential public health early warning system for seasonal norovirus activity in Ontario, Canada.Epidemiology and infection · 2019Article
- The role of connected diagnostics in strengthening regional, national and continental African disease surveillance.African journal of laboratory medicine · 2018Article
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4 authors.
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Abstract
backgroundChina Centre for Diseases Control and Prevention (CDC) developed the China Infectious Disease Automated Alert and Response System (CIDARS) in 2005. The CIDARS was used to strengthen infectious disease surveillance and aid in the early warning of outbreak. The CIDARS has been integrated into the routine outbreak monitoring efforts of the CDC at all levels in China. Early warning threshold is crucial for outbreak detection in the CIDARS, but CDCs at all level are currently using thresholds recommended by the China CDC, and these recommended thresholds have recognized limitations. Our study therefore seeks to explore an operational method to select the proper early warning threshold according to the epidemic features of local infectious diseases.
methodsThe data used in this study were extracted from the web-based Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), and data for infectious disease cases were organized by calendar week (1-52) and year (2009-2015) in Excel format; Px was calculated using a percentile-based moving window (moving window [5 week*5 year], x), where x represents one of 12 centiles (0.40, 0.45, 0.50….0.95). Outbreak signals for the 12 Px were calculated using the moving percentile method (MPM) based on data from the CIDARS. When the outbreak signals generated by the 'mean + 2SD' gold standard were in line with a Px generated outbreak signal for each week during the year of 2014, this Px was then defined as the proper threshold for the infectious disease. Finally, the performance of new selected thresholds for each infectious disease was evaluated by simulated outbreak signals based on 2015 data.
resultsSix infectious diseases were selected in this study (chickenpox, mumps, hand foot and mouth diseases (HFMD), scarlet fever, influenza and rubella). Proper thresholds for chickenpox (P75), mumps (P80), influenza (P75), rubella (P45), HFMD (P75), and scarlet fever (P80) were identified. The selected proper thresholds for these 6 infectious diseases could detect almost all simulated outbreaks within a shorter time period compared to thresholds recommended by the China CDC.
conclusionsIt is beneficial to select the proper early warning threshold to detect infectious disease aberrations based on characteristics and epidemic features of local diseases in the CIDARS.
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