Header

UZH-Logo

Maintenance Infos

Bayesian Network Modeling applied to Feline Calicivirus Infection among cats in Switzerland


Kratzer, Gilles; Lewis, Fraser I; Willi, Barbara; Meli, Marina L; Boretti, Felicitas S; Hofmann-Lehmann, Regina; Torgerson, Paul R; Furrer, Reinhard; Hartnack, Sonja (2020). Bayesian Network Modeling applied to Feline Calicivirus Infection among cats in Switzerland. Frontiers in Veterinary Science, 7:73.

Abstract

Bayesian network (BN) modeling is a rich and flexible analytical framework capable ofelucidating complex veterinary epidemiological data. It is a graphical modeling techniquethat enables the visual presentation of multi-dimensionalresults while retaining statisticalrigor in population-level inference. Using previously published case study data aboutfeline calicivirus (FCV) and other respiratory pathogens in cats in Switzerland, a full BNmodeling analysis is presented. The analysis shows that reducing the group size andvaccinating animals are the two actionable factors directly associated with FCV statusand are primary targets to control FCV infection. The presence of gingivostomatitis andMycoplasma felisis also associated with FCV status, but signs of upper respiratorytract disease (URTD) are not. FCV data is particularly well-suited to a network modelingapproach, as both multiple pathogens and multiple clinicalsigns per pathogen areinvolved, along with multiple potentially interrelated risk factors. BN modeling is aholistic approach—all variables of interest may be mutuallyinterdependent—whichmay help to address issues, such as confounding and collinear factors, as well as todisentangle directly vs. indirectly related variables. Weintroduce the BN methodology asan alternative to the classical uni- and multivariable regression approaches commonlyused for risk factor analyses. We advise and guide researchers about how to use BNsas an exploratory data tool and demonstrate the limitationsand practical issues. Wepresent a step-by-step case study using FCV data along with all code necessary toreproduce our analyses in the open-source R environment. Wecompare and contrastthe findings of the current case study using BN modeling with previous results thatused classical regression techniques, and we highlight newpotential insights. Finally,we discuss advanced methods, such as Bayesian model averaging, a common way ofaccounting for model uncertainty in a Bayesian network context.

Abstract

Bayesian network (BN) modeling is a rich and flexible analytical framework capable ofelucidating complex veterinary epidemiological data. It is a graphical modeling techniquethat enables the visual presentation of multi-dimensionalresults while retaining statisticalrigor in population-level inference. Using previously published case study data aboutfeline calicivirus (FCV) and other respiratory pathogens in cats in Switzerland, a full BNmodeling analysis is presented. The analysis shows that reducing the group size andvaccinating animals are the two actionable factors directly associated with FCV statusand are primary targets to control FCV infection. The presence of gingivostomatitis andMycoplasma felisis also associated with FCV status, but signs of upper respiratorytract disease (URTD) are not. FCV data is particularly well-suited to a network modelingapproach, as both multiple pathogens and multiple clinicalsigns per pathogen areinvolved, along with multiple potentially interrelated risk factors. BN modeling is aholistic approach—all variables of interest may be mutuallyinterdependent—whichmay help to address issues, such as confounding and collinear factors, as well as todisentangle directly vs. indirectly related variables. Weintroduce the BN methodology asan alternative to the classical uni- and multivariable regression approaches commonlyused for risk factor analyses. We advise and guide researchers about how to use BNsas an exploratory data tool and demonstrate the limitationsand practical issues. Wepresent a step-by-step case study using FCV data along with all code necessary toreproduce our analyses in the open-source R environment. Wecompare and contrastthe findings of the current case study using BN modeling with previous results thatused classical regression techniques, and we highlight newpotential insights. Finally,we discuss advanced methods, such as Bayesian model averaging, a common way ofaccounting for model uncertainty in a Bayesian network context.

Statistics

Citations

Altmetrics

Downloads

6 downloads since deposited on 02 Apr 2020
6 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
07 Faculty of Science > Institute for Computational Science
05 Vetsuisse Faculty > Veterinary Clinic > Department of Small Animals
05 Vetsuisse Faculty > Chair in Veterinary Epidemiology
05 Vetsuisse Faculty > Veterinary Clinic > Department of Clinical Diagnostics and Services
05 Vetsuisse Faculty > Center for Clinical Studies
Dewey Decimal Classification:570 Life sciences; biology
630 Agriculture
Scopus Subject Areas:Health Sciences > General Veterinary
Uncontrolled Keywords:Bayesian network; feline calicivirus; good modeling practice; graphical model; multivariable analysis; reproducible research; risk factor analysis
Language:English
Date:26 February 2020
Deposited On:02 Apr 2020 14:16
Last Modified:11 May 2020 19:49
Publisher:Frontiers Research Foundation
ISSN:2297-1769
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.3389/fvets.2020.00073
PubMed ID:32175337

Download

Gold Open Access

Download PDF  'Bayesian Network Modeling applied to Feline Calicivirus Infection among cats in Switzerland'.
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 1MB
View at publisher
Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)