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

Publikation: Konferencebidrag › Paper › Forskning › fagfællebedømt

Standard

Dynamic generalized linear models for monitoring endemic diseases : moving beyond univariate process monitoring control algorithms. / Lopes Antunes, Ana Carolina; Jensen, Dan Børge; Halasa, Tariq; Toft, Nina.

2016. Paper præsenteret ved Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016.

Publikation: Konferencebidrag › Paper › Forskning › fagfællebedømt

Harvard

Lopes Antunes, AC, Jensen, DB, Halasa, T & Toft, N 2016, 'Dynamic generalized linear models for monitoring endemic diseases: moving beyond univariate process monitoring control algorithms', Paper fremlagt ved Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016, 16/03/2016 - 18/03/2017. <https://www.researchgate.net/publication/298744456_DYNAMIC_GENERALIZED_LINEAR_MODELS_FOR_MONITORING_ENDEMIC_DISEASES_MOVING_BEYOND_UNIVARIATE_PROCESS_MONITORING_CONTROL_ALGORITHMS>

APA

Lopes Antunes, A. C., Jensen, D. B., Halasa, T., & Toft, N. (2016). Dynamic generalized linear models for monitoring endemic diseases: moving beyond univariate process monitoring control algorithms. Paper præsenteret ved Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016. https://www.researchgate.net/publication/298744456_DYNAMIC_GENERALIZED_LINEAR_MODELS_FOR_MONITORING_ENDEMIC_DISEASES_MOVING_BEYOND_UNIVARIATE_PROCESS_MONITORING_CONTROL_ALGORITHMS

Vancouver

Lopes Antunes AC, Jensen DB, Halasa T, Toft N. Dynamic generalized linear models for monitoring endemic diseases: moving beyond univariate process monitoring control algorithms. 2016. Paper præsenteret ved Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016.

Author

Lopes Antunes, Ana Carolina ; Jensen, Dan Børge ; Halasa, Tariq ; Toft, Nina. / Dynamic generalized linear models for monitoring endemic diseases : moving beyond univariate process monitoring control algorithms. Paper præsenteret ved Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016.11 s.

Bibtex

@conference{dbdcb25991444a8fb181e2c40ec4d4f4,
title = "Dynamic generalized linear models for monitoring endemic diseases: moving beyond univariate process monitoring control algorithms",
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.",
author = "{Lopes Antunes}, {Ana Carolina} and Jensen, {Dan B{\o}rge} and Tariq Halasa and Nina Toft",
year = "2016",
month = mar,
language = "English",
note = "null ; Conference date: 16-03-2016 Through 18-03-2017",

}

RIS

TY - CONF

T1 - Dynamic generalized linear models for monitoring endemic diseases

AU - Lopes Antunes, Ana Carolina

AU - Jensen, Dan Børge

AU - Halasa, Tariq

AU - Toft, Nina

PY - 2016/3

Y1 - 2016/3

N2 - 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.

AB - 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.

UR - https://curis.ku.dk/admin/files/174127172/SVEPM_Proceedings_Antunes.pdf

M3 - Paper

Y2 - 16 March 2016 through 18 March 2017

ER -

ID: 174127170