Assessment of Several Algorithms for Outbreak Detection using Bovine Meat Inspection Data for Syndromic Surveillance: A Pilot Study on Whole Carcass Condemnation Rate | Zendy

Céline Dupuy | Zendy; Eric Morignat | Zendy; Fernanda C. Dórea | Zendy; Christian Ducrot | Zendy; Didier Calavas | Zendy; Emilie Gay | Zendy

Open Access

Assessment of Several Algorithms for Outbreak Detection using Bovine Meat Inspection Data for Syndromic Surveillance: A Pilot Study on Whole Carcass Condemnation Rate

Author(s) -

Céline Dupuy,

Eric Morignat,

Fernanda C. Dórea,

Christian Ducrot,

Didier Calavas,

Emilie Gay

Publication year - 2015

Publication title -

online journal of public health informatics

Language(s) - English

Resource type - Journals

ISSN - 1947-2579

DOI - 10.5210/ojphi.v7i1.5791

Subject(s) - cusum , ewma chart , negative binomial distribution , statistics , confidence interval , outbreak , control chart , count data , algorithm , medicine , mathematics , veterinary medicine , computer science , process (computing) , poisson distribution , virology , operating system

Slaughterhouses are a potential source of data which is under-used for cattle health monitoring. The objective of this work was to assess the performance of several algorithms for outbreak detection based on weekly proportions of whole carcass condemnation. Data from 177,098 cattle slaughtered in one French slaughterhouse from 2005 to 2009 were used. The Shewart p chart, one-sided confidence interval of a negative binomial regression model, and EWMA and CUSUM on residuals of a negative binomial model were investigated. The highest sensitivity was obtained using negative binomial regression and the highest specificity using CUSUM or EWMA.

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