Dynamic generalized linear models for monitoring endemic diseases: moving beyond univariate process monitoring control algorithms

Ana Carolina Lopes Antunes, Dan Børge Jensen, Tariq Halasa, Nina Toft

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    Abstract

    The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control and eradication programmes based on changes in PRRS sero-prevalence was explored. Results showed a declining trend in PRRS sero-prevalence between 2007 and 2014 suggesting that Danish herds are slowly eradicating PRRS. The simulation study demonstrated the flexibility of DGLMs in adapting to changes intrends in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance of endemic and (re-)emerging diseases.
    Original languageEnglish
    Publication dateMar 2016
    Number of pages11
    Publication statusPublished - Mar 2016
    EventAnnual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016 -
    Duration: 16 Mar 201618 Mar 2017

    Conference

    ConferenceAnnual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016
    Period16/03/201618/03/2017

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