Jensen, Vibeke

Lopes Antunes, Ana Carolina

Toft, Nils

The aim of this study was to explore the potential of using multiple data sources for monitoring swine disease

Online Research Database In Technology

                       General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal   If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.     Downloaded from orbit.dtu.dk on: Jun 17, 2018Can we detect outbreaks at herd-level earlier when combining multiple data sources?Lopes Antunes, Ana Carolina; Jensen, Vibeke Frøkjær; Toft, NilsPublished in:ISESSAH-InnovSur 2018 Conference Abstract BookPublication date:2018Link back to DTU OrbitCitation (APA):Lopes Antunes, A. C., Jensen, V., & Toft, N. (2018). Can we detect outbreaks at herd-level earlier whencombining multiple data sources? In ISESSAH-InnovSur 2018 Conference Abstract Book [S2-P35]innovSur 2018 SeSSiOn 2 - Research and surveillance policiesISESSAH-InnovSur 2018 ConfErEnCE  |  14-18 May 2018 - Supagro Montpellier, France  |  ABSTRACT BOOK48S2-P35Can we detect outbreaks at herd-level earlier when combining multiple data sources?   Ana Carolina Lopes Antunes*, Vibeke Jensen, Nils Toft  National Veterinary Institute - DTU, Kgs. Lyngby, Denmark  *Corresponding author: aclan@vet.dtu.dk (Ana Carolina Lopes Antunes)  The aim of this study was to explore the potential of using multiple data sources for monitoring swine diseases. A total of 81 Danish breeding herds with known changes in disease status were included in the study. Disease status was defined based on monthly serology diagnostic tests performed between January 2014 and September 2017. Changes in mortality data, as well as antimicrobial consumption and vaccine use at herd-level, were monitored in herds that became positive for Porcine Reproductive and Respiratory Syndrome (PRRS), enzootic pneumonia (Mycoplasma hyopneumonia) and porcine pleuropneumonia (Actinobacillus pluropneumoniae). The analysis was run separately for each age-group - weaners (up to 30kg), sows and finishers - within each herd and alarms were generated based on Shewhart control charts. The alarms from the multiple data streams were integrated before, during and after the herds changed status with respect to a disease. An increase of alarms based on mortality and antimicrobial use for respiratory diseases was seen for sows and finishers two months before becoming positive for PRRS. For enzootic pneumonia, increases in the number of alarms for weaners, sows, and finishers based on mortality and antimicrobial consumption were found 3, 2 and 1 months before these herds changed their official status. Increases in the number of alarms based on mortality data, as well as antimicrobial and vaccine use were seen in the same month when sows and weaners became positive for porcine pleuropneumonia. Monitoring changes in mortality data, as well as antimicrobial consumption and vaccine use, evidenced changes in herd-level suggestive of on-going outbreaks before diagnostic test confirmation. The integration of multiple data sources is a good proxy for disease occurrence deriving better monitoring and surveillance systems and control and eradication programs at a national scale.    Research impact highlights: Monitoring existing animal health databases (i.e. big data) is a growing field, providing a more cost-effective alternative to collect primary data. Monitoring changes in mortality data, as well as antimicrobial consumption and vaccine use in Danish breeding herds, evidenced changes in herd-level suggestive of on-going outbreaks before diagnostic test confirmation. The integration of multiple data sources is a good proxy for disease occurrence deriving better monitoring and surveillance systems and control and eradication programs at a national scale. These findings are closely linked to the conference themes of “Surveillance design and big data” and “Research and surveillance policies”.  Keywords: outbreaks - early warning - big data - swine 

Can we detect outbreaks at herd-level earlier when combining multiple data sources?

http://orbit.dtu.dk/ws/files/148420082/InnovSur_2018_AbstractBook_ACLA.pdf

Can we detect outbreaks at herd-level earlier when combining multiple data sources?

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