Statistical Modelling 23 (1) (2023), 31–52

A spatially explicit N-mixture model for the estimation of disease prevalence

Ben J Brintz,
Division of Epidemiology,
University of Utah,
Salt Lake City,
Utah,
USA.
e-mail: ben.brintz@hsc.utah.edu

Lisa Madsen,
Department of Statistics,
Oregon State University,
Corvallis,
Oregon,
USA.

Claudio Fuentes,
Department of Statistics,
Oregon State University,
Corvallis,
Oregon,
USA.

Abstract:

This article develops an approximate N-mixture model for infectious disease counts that accounts for under-reporting as well as spatial dependence induced by person-to-person spread of disease. We employ the model to estimate actual case counts in Oregon of chlamydia, an easily-treated but usually asymptomatic sexually transmitted disease. We describe a combined parametric bootstrap to account for uncertainty in parameter estimates as well as sampling variability in actual case counts. A simulation study illustrates that our method performs well in many scenarios when the model is correctly specified, and also gives reasonable results when the model is misspecified, and no spatial dependence exists.

Keywords:

Case counts, Chlamydia, normal approximation, Oregon, parametric bootstrap.

Downloads:

Code and data in zipped archive.

back