title: Outbreak definition by change point analysis: a tool for public health decision?
abstract: BACKGROUND
  Most 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.
  
  
  METHODS
  We 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).
  
  
  RESULTS
  A 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.
  
  
  CONCLUSIONS
  For 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.
authors.affiliation: Centre Pasteur du Cameroun, BP 1274, Yaoundé, Cameroon. gaetex1@gmail.com.|UMR 912/SESSTIM - INSERM/IRD/Aix-Marseille Université/Faculty of Medicine, 27, Bd Jean Moulin, 13385, Marseille, France. gaetex1@gmail.com.
authors.forename: Gaëtan
authors.identifier: http://orcid.org/0000-0002-9242-1018
authors.initials: G
authors.lastname: Texier
authors.affiliation: Institut Sous-régional de Statistique et d'Économie Appliquée, BP 294, Yaoundé, Cameroon.
authors.forename: Magnim
authors.initials: M
authors.lastname: Farouh
authors.affiliation: Center for Epidemiology and Public Health of the French Army (CESPA), Camp de Sainte Marthe, 13568, Marseille, France.
authors.forename: Liliane
authors.initials: L
authors.lastname: Pellegrin
authors.affiliation: Group Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, USA.
authors.forename: Michael L
authors.initials: ML
authors.lastname: Jackson
authors.affiliation: Center for Epidemiology and Public Health of the French Army (CESPA), Camp de Sainte Marthe, 13568, Marseille, France.
authors.forename: Jean-Baptiste
authors.initials: JB
authors.lastname: Meynard
authors.affiliation: UMR 912/SESSTIM - INSERM/IRD/Aix-Marseille Université/Faculty of Medicine, 27, Bd Jean Moulin, 13385, Marseille, France.|Center for Epidemiology and Public Health of the French Army (CESPA), Camp de Sainte Marthe, 13568, Marseille, France.
authors.forename: Xavier
authors.initials: X
authors.lastname: Deparis
authors.affiliation: UMR 912/SESSTIM - INSERM/IRD/Aix-Marseille Université/Faculty of Medicine, 27, Bd Jean Moulin, 13385, Marseille, France.|Center for Epidemiology and Public Health of the French Army (CESPA), Camp de Sainte Marthe, 13568, Marseille, France.
authors.forename: Hervé
authors.initials: H
authors.lastname: Chaudet
mesh_terms: D003198:Computer Simulation; D003661:Decision Support Techniques; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D006801:Humans; D015233:Models, Statistical; D011634:Public Health
keywords: Change point analysis; Disease surveillance; Evaluation; Expert; Outbreak identification