Literature DB >> 27063588

A system for automated outbreak detection of communicable diseases in Germany.

Maëlle Salmon1, Dirk Schumacher, Hendrik Burmann, Christina Frank, Hermann Claus, Michael Höhle.   

Abstract

We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute.

Keywords:  automated surveillance; epidemiology; informatics; outbreaks; software; statistics; surveillance

Mesh:

Year:  2016        PMID: 27063588     DOI: 10.2807/1560-7917.ES.2016.21.13.30180

Source DB:  PubMed          Journal:  Euro Surveill        ISSN: 1025-496X


  10 in total

1.  Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in Germany.

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2.  Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study.

Authors:  Gabriel Bédubourg; Yann Le Strat
Journal:  PLoS One       Date:  2017-07-17       Impact factor: 3.240

3.  Timeliness in the German surveillance system for infectious diseases: Amendment of the infection protection act in 2013 decreased local reporting time to 1 day.

Authors:  Jakob Schumacher; Michaela Diercke; Maëlle Salmon; Irina Czogiel; Dirk Schumacher; Hermann Claus; Andreas Gilsdorf
Journal:  PLoS One       Date:  2017-10-31       Impact factor: 3.240

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Authors:  Olivera Stojanović; Johannes Leugering; Gordon Pipa; Stéphane Ghozzi; Alexander Ullrich
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Review 5.  Notifiable Diseases Surveillance System with a Data Architecture Approach: a Systematic Review.

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6.  Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection.

Authors:  Thibaut Jombart; Stéphane Ghozzi; Dirk Schumacher; Timothy J Taylor; Quentin J Leclerc; Mark Jit; Stefan Flasche; Felix Greaves; Tom Ward; Rosalind M Eggo; Emily Nightingale; Sophie Meakin; Oliver J Brady; Graham F Medley; Michael Höhle; W John Edmunds
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2021-05-31       Impact factor: 6.237

7.  Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

Authors:  Doyo G Enki; Paul H Garthwaite; C Paddy Farrington; Angela Noufaily; Nick J Andrews; Andre Charlett
Journal:  PLoS One       Date:  2016-08-11       Impact factor: 3.240

8.  Mortality, morbidity and health in developed societies: a review of data sources.

Authors:  Guillaume Wunsch; Catherine Gourbin
Journal:  Genus       Date:  2018-01-29

9.  Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System.

Authors:  Ruiping Wang; Yonggen Jiang; Xiaoqin Guo; Yiling Wu; Genming Zhao
Journal:  J Int Med Res       Date:  2017-07-21       Impact factor: 1.671

10.  Clinical Epidemiology of 7126 Melioidosis Patients in Thailand and the Implications for a National Notifiable Diseases Surveillance System.

Authors:  Viriya Hantrakun; Somkid Kongyu; Preeyarach Klaytong; Sittikorn Rongsumlee; Nicholas P J Day; Sharon J Peacock; Soawapak Hinjoy; Direk Limmathurotsakul
Journal:  Open Forum Infect Dis       Date:  2019-11-19       Impact factor: 3.835

  10 in total

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