ArticleBMC medical informatics and decision making2016
Outbreak definition by change point analysis: a tool for public health decision?
Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Ensemble-labeling of infectious disease time series to evaluate early warning systems.Infectious Disease Modelling · 2026Article
- Multiple change point analysis and time series forecast of brucellosis reports in Xinjiang, China from 2010 to 2024.BMC public health · 2025Article
- Bayesian changepoint detection for epidemic models.Scientific reports · 2025Article
- Longitudinal host-microbiome dynamics of metatranscription identify hallmarks of progression in periodontitis.Microbiome · 2025Article
- Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm.Scientific reports · 2025Article
- Predicting the spread of SARS-CoV-2 variants: An artificial intelligence enabled early detection.PNAS nexus · 2024Article
- Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods.Veterinary research · 2023Article
- Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021.Frontiers in public health · 2023Article
- Epidemic changepoint detection in the presence of nuisance changes.Statistical papers (Berlin, Germany) · 2023Article
- Spatiotemporal dynamics of malaria in Zanzibar, 2015-2020.BMJ global health · 2023Article
- Article
- Multiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions.Spatial statistics · 2022Article
- Real-Time Detection of Flu Season Onset: A Novel Approach to Flu Surveillance.International journal of environmental research and public health · 2022Article
- Early warning signals of infectious disease transitions: a review.Journal of the Royal Society, Interface · 2021Review
- Contextual factors and the COVID-19 outbreak rate across U.S. counties in its initial phase.Health science reports · 2021Article
- Outbreak minimization v.s. influence maximization: an optimization framework.BMC medical informatics and decision making · 2020Article
- Variation in COVID-19 outbreaks at the US state and county levels.Public health · 2020Article
- Complicated Alcohol Withdrawal-An Unintended Consequence of COVID-19 Lockdown.Alcohol and alcoholism (Oxford, Oxfordshire) · 2020Article
- Using decision fusion methods to improve outbreak detection in disease surveillance.BMC medical informatics and decision making · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost studies of epidemic detection focus on their start and rarely on the whole signal or the end of the epidemic. In some cases, it may be necessary to retrospectively identify outbreak signals from surveillance data. Our study aims at evaluating the ability of change point analysis (CPA) methods to locate the whole disease outbreak signal. We will compare our approach with the results coming from experts' signal inspections, considered as the gold standard method.
methodsWe simulated 840 time series, each of which includes an epidemic-free baseline (7 options) and a type of epidemic (4 options). We tested the ability of 4 CPA methods (Max-likelihood, Kruskall-Wallis, Kernel, Bayesian) methods and expert inspection to identify the simulated outbreaks. We evaluated the performances using metrics including delay, accuracy, bias, sensitivity, specificity and Bayesian probability of correct classification (PCC).
resultsA minimum of 15 h was required for experts for analyzing the 840 curves and a maximum of 25 min for a CPA algorithm. The Kernel algorithm was the most effective overall in terms of accuracy, bias and global decision (PCC = 0.904), compared to PCC of 0.848 for human expert review.
conclusionsFor the aim of retrospectively identifying the start and end of a disease outbreak, in the absence of human resources available to do this work, we recommend using the Kernel change point model. And in case of experts' availability, we also suggest to supplement the Human expertise with a CPA, especially when the signal noise difference is below 0.
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