Statistics and Its Interface

Volume 12 (2019)

Number 1

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

Pages: 11 – 19

DOI: https://dx.doi.org/10.4310/SII.2019.v12.n1.a2

Authors

Yaxuan Sun (Department of Statistics, Iowa State University, Ames, Ia., U.S.A.)

Chong Wang (Department of Statistics, Department of Veterninary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Ia., U.S.A.)

William Q. Meeker (Department of Statistics, Iowa State University, Ames, Ia., U.S.A.)

Max Morris (Department of Statistics, Iowa State University, Ames, Ia., U.S.A.)

Marisa L. Rotolo (Department of Statistics, Department of Veterninary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Ia., U.S.A.)

Jeffery Zimmerman (Department of Statistics, Department of Veterninary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Ia., U.S.A.)

Abstract

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.

Keywords

Bayesian, disease surveillance, interval-censored data, misclassification, piecewise exponential model, spatial

Received 25 May 2017

Published 26 October 2018