Department of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Columbia, 29208, SC, USA.
College of Veterinary Medicine, Kansas State University, 1700 Denison Ave, Manhattan, 66502, KS, USA.
Spat Spatiotemporal Epidemiol. 2024 Aug;50:100677. doi: 10.1016/j.sste.2024.100677. Epub 2024 Jul 23.
Spatial patterns are common in infectious disease epidemiology. Disease mapping is essential to infectious disease surveillance. Under a group testing protocol, biomaterial from multiple individuals is physically combined into a pooled specimen, which is then tested for infection. If the pool tests negative, all contributing individuals are generally assumed to be uninfected. If the pool tests positive, the individuals are usually retested to determine who is infected. When the prevalence of infection is low, group testing provides significant cost savings over traditional individual testing by reducing the number of tests required. However, the lack of statistical methods capable of producing maps from group testing data has limited the use of group testing in disease mapping. We develop a Bayesian methodology that can simultaneously map disease prevalence using group testing data and identify risk factors for infection. We illustrate its real-world utility using two datasets from vector-borne disease surveillance.
空间模式在传染病流行病学中很常见。疾病绘图对于传染病监测至关重要。在分组检测方案下，将来自多个个体的生物材料物理组合成一个混合样本，然后对其进行感染检测。如果混合样本检测结果为阴性，则通常假定所有参与个体均未感染。如果混合样本检测结果为阳性，则通常会对个体进行再次检测以确定谁被感染。当感染率较低时，与传统的个体检测相比，分组检测通过减少所需的检测数量，可显著节省成本。然而，缺乏能够从分组检测数据生成图谱的统计方法，限制了分组检测在疾病绘图中的应用。我们开发了一种贝叶斯方法，该方法可以同时使用分组检测数据来绘制疾病流行率图，并确定感染的危险因素。我们使用来自虫媒疾病监测的两个数据集来说明其实用性。
