Automated Biosurveillance Data from England and Wales, 1991–2011

Enki, Doyo; Noufaily, Angela; Garthwaite, Paul H.; Andrews, Nick; Charlett, André; Lane, Chris; Farrington, Paddy

doi:10.3201/eid1901.120493

Cited by 21 publications

(33 citation statements)

References 15 publications

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“…falciparum , or in case of rare pathogens. These issues are planed to be addressed with the development of a local web-based plateform including more sophisticated statistical tools for the accurate monitoring of abnormal events [22–24]. We also plan to integrate local Health Agencies to our surveillance network (Fig 3) in order to actively participate to the Senegal national surveillance of infectious diseases like it is the case in France [11].…”

Section: Discussionmentioning

confidence: 99%

Implementation of Syndromic Surveillance Systems in Two Rural Villages in Senegal

et al. 2016

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Infectious diseases still represent a major challenge for humanity. In this context, their surveillance is critical. From 2010 to 2016, two Point-Of-Care (POC) laboratories have been successfully implemented in the rural Saloum region of Senegal. In parallel, a homemade syndromic surveillance system called EPIMIC was implemented to monitor infectious diseases using data produced by the POC laboratory of the Timone hospital in Marseille, France. The aim of this study is to describe the steps necessary for implementing EPIMIC using data routinely produced by two POC laboratories (POC-L) established in rural Senegal villages. After improving EPIMIC, we started to monitor the 15 pathogens routinely diagnosed in the two POC-L using the same methodology we used in France. In 5 years, 2,577 deduplicated patients-samples couples from 775 different patients have been tested in the Dielmo and Ndiop POC-L. 739 deduplicated patients-samples couples were found to be positive to at least one of the tested pathogens. The retrospective analysis of the Dielmo and Ndiop POC data with EPIMIC allowed to generate 443 alarms. Since January 2016, 316 deduplicated patients-samples couples collected from 298 different patients were processed in the Niakhar POC laboratory. 56 deduplicated patients-samples couples were found to be positive to at least one of the tested pathogens. The retrospective analysis of the data of the Niakhar POC laboratory with EPIMIC allowed to generate 14 alarms. Although some improvements are still needed, EPIMIC has been successfully spread using data routinely produced by two rural POC-L in Senegal, West Africa.

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Section: Discussionmentioning

confidence: 99%

Implementation of Syndromic Surveillance Systems in Two Rural Villages in Senegal

et al. 2016

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“…It is a collaborative system (one relying on the participation of private and hospital laboratories) which is different from BALYSES (a hospital system) ( 10 ). Similar biosurveillance networks were developed in Belgium ( 14 , 24 ), the United Kingdom ( 13 ), and the United States, where the Laboratory Response Network was implemented by the Centers for Disease Control and Prevention in 1999 ( 25 ). In Belgium, a sentinel network using microbiology laboratory data was created for the weekly monitoring of selected pathogens ( 24 , 26 ).…”

Section: Discussionmentioning

confidence: 99%

“…At the beginning of the surveillance system data collection, in the absence of strong historical data, this basic algorithm seemed to be the most appropriate and easy to use given the circumstances. After studying the methods used to address seasonal variations and sporadic emergence of rare bacterial species as described by Enki et al ( 13 ) in 2013, Farrington et al ( 28 ) in 1996, Buckeridge et al ( 29 ) in 2004, and Frickers ( 30 ) in 2008, we decided to implement another method and this introduced the C1-mild epidemics detection method ( 18 ) with R software into the surveillance package. This method is now used routinely in CESPA.…”

Section: Discussionmentioning

confidence: 99%

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Implementation and Initial Analysis of a Laboratory-Based Weekly Biosurveillance System, Provence-Alpes-Côte d’Azur, France

Huart¹,

Bédubourg²,

Abat³

et al. 2017

Emerg. Infect. Dis.

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We describe the implementation of an automated infectious disease surveillance system that uses data collected from 210 microbiologic laboratories throughout the Provence-Alpes-Côte d’Azur region in France. Each week, these facilities report bacterial species that have been isolated from patients in their area. An alarm is triggered whenever the case count for a bacterial species infection exceeds 2 SDs of the historical mean for that species at the participating laboratory. At its inception in July 2013, the system monitored 611 bacterial species. During July 1, 2013–March 20, 2016, weekly analyses of incoming surveillance data generated 34 alarms signaling possible infectious disease outbreaks; after investigation, 14 (41%) of these alarms resulted in health alerts declared by the regional health authority. We are currently improving the system by developing an Internet-based surveillance platform and extending our surveillance to include more laboratories in the region.

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“…However, these statistics do not consider seasonal variations in pathogen isolation, especially for rare bacterial species. To address this problem, Enki et al improved the detection algorithms according to the frequency of isolation of the 3,303 pathogens included in the 20-year LabBase surveillance database recovered from the UK Health Protection Agency ( 26 ). They discovered that although all of these organisms varied greatly in their isolation frequency, most of them could be surveyed by using quasi-Poisson or negative binomial models for which the variance is proportional to the mean.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France

Abat¹,

Chaudet²,

Colson³

et al. 2015

Emerg. Infect. Dis.

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Automated Biosurveillance Data from England and Wales, 1991–2011

Abstract: Twenty years of data provide valuable insights for the design of large automated outbreak detection systems.

Cited by 21 publications

References 15 publications

Implementation of Syndromic Surveillance Systems in Two Rural Villages in Senegal

Implementation of Syndromic Surveillance Systems in Two Rural Villages in Senegal

Implementation and Initial Analysis of a Laboratory-Based Weekly Biosurveillance System, Provence-Alpes-Côte d’Azur, France

Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France

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