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.

本文为传染病计数开发了一个近似的 N 混合模型，该模型解释了疾病在人与人之间传播引起的漏报和空间依赖性。我们使用该模型来估计俄勒冈州衣原体的实际病例数，衣原体是一种易于治疗但通常无症状的性传播疾病。我们描述了一个组合参数引导程序，以解决参数估计中的不确定性以及实际案例计数中的抽样变异性。仿真研究表明，当模型正确指定时，我们的方法在许多场景中表现良好，并且在模型指定错误时也给出了合理的结果，并且不存在空间依赖性。