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

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Sun, Yaxuan
Morris, Max
Rotolo, Marisa
Zimmerman, Jeffery
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Meeker, William
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As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
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Veterinary Diagnostic and Production Animal Medicine
The mission of VDPAM is to educate current and future food animal veterinarians, population medicine scientists and stakeholders by increasing our understanding of issues that impact the health, productivity and well-being of food and fiber producing animals; developing innovative solutions for animal health and food safety; and providing the highest quality, most comprehensive clinical practice and diagnostic services. Our department is made up of highly trained specialists who span a wide range of veterinary disciplines and species interests. We have faculty of all ranks with expertise in diagnostics, medicine, surgery, pathology, microbiology, epidemiology, public health, and production medicine. Most have earned certification from specialty boards. Dozens of additional scientists and laboratory technicians support the research and service components of our department.
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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.


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." Statistics and Its Interface 12, no. 1 (2019): 11-19. DOI: 10.4310/SII.2019.v12.n1.a2. Posted with permission.

Mon Jan 01 00:00:00 UTC 2018