A latent spatial piecewise exponential model for interval-censored disease surveillance data with time-varying covariates and misclassification

dc.contributor.author Wang, Chong
dc.contributor.author Sun, Yaxuan
dc.contributor.author Wang, Chong
dc.contributor.author Meeker, William
dc.contributor.author Morris, Max
dc.contributor.author Meeker, William
dc.contributor.author Rotolo, Marisa
dc.contributor.author Zimmerman, Jeffery
dc.contributor.department Statistics
dc.contributor.department Veterinary Diagnostic and Production Animal Medicine
dc.date 2018-11-27T18:55:21.000
dc.date.accessioned 2020-07-07T05:12:45Z
dc.date.available 2020-07-07T05:12:45Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 2019-01-01
dc.description.abstract <p>Understanding the dynamics of disease spread is critical to achieving effective animal disease surveillance. A major challenge in modeling disease spread is the fact that the true disease status cannot be known with certainty due to the imperfect diagnostic sensitivity and specificity of the tests used to generate the disease surveillance data. Other challenges in modeling such data include interval censoring, relating disease spread to distance between units, and incorporating time-varying covariates, which are the unobserved disease statuses. We propose a latent spatial piecewise exponential model (PEX) with misclassification of events to address the challenges in modeling such disease surveillance data. Specifically, a piecewise exponential model is used to describe the latent disease process, with spatial distance and timevarying covariates incorporated for disease spread. The observed surveillance data with imperfect diagnostic tests are then modeled using a binary misclassification process given the latent disease statuses from the PEX model. Model parameters are estimated through a Bayesian approach utilizing non-informative priors. A simulation study is performed to evaluate the model performance and the results are compared with a candidate model where no misclassification is considered. For further illustration, we discuss an application of this model to a porcine reproductive and respiratory syndrome virus (PRRSV) surveillance data collected from commercial swine farms.</p>
dc.description.comments <p>This article is published as Sun, Yaxuan, Chong Wang, William Q. Meeker, Max Morris, Marisa L. Rotolo, and Jeffery Zimmerman. "A latent spatial piecewise exponential model for interval-censored disease surveillance data with time-varying covariates and misclassification." <em>Statistics and Its Interface</em> 12, no. 1 (2019): 11-19. DOI: <a href="https://dx.doi.org/10.4310/SII.2019.v12.n1.a2" target="_blank">10.4310/SII.2019.v12.n1.a2</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/vdpam_pubs/126/
dc.identifier.articleid 1129
dc.identifier.contextkey 13375597
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath vdpam_pubs/126
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/91968
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/vdpam_pubs/126/0-2018_WangChong_PermGrant_LatentSpatial_IntlPress.pdf|||Fri Jan 14 19:25:42 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/vdpam_pubs/126/2019_WangChong_LatentSpatial.pdf|||Fri Jan 14 19:25:44 UTC 2022
dc.source.uri 10.4310/SII.2019.v12.n1.a2
dc.subject.disciplines Applied Statistics
dc.subject.disciplines Large or Food Animal and Equine Medicine
dc.subject.disciplines Veterinary Pathology and Pathobiology
dc.subject.disciplines Veterinary Preventive Medicine, Epidemiology, and Public Health
dc.subject.keywords Bayesian
dc.subject.keywords disease surveillance
dc.subject.keywords interval-censored data
dc.subject.keywords misclassification
dc.subject.keywords piecewise exponential model
dc.subject.keywords spatial
dc.title A latent spatial piecewise exponential model for interval-censored disease surveillance data with time-varying covariates and misclassification
dc.type article
dc.type.genre article
dspace.entity.type Publication
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