Department of Mathematical Sciences, Pan African University Institute of Basic Sciences Technology and Innovation, Nairobi, Kenya.
School of Mathematics, Statistics & Computer Science, University of KwaZulu Natal, Pietermaritzburg, South Africa.
PLoS One. 2020 Jul 2;15(7):e0234456. doi: 10.1371/journal.pone.0234456. eCollection 2020.
The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012-2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.
同时对多种相关疾病进行时空建模通过在相关疾病之间借用信息来加强推断。许多针对时空建模方法的研究贡献以不同的方式展示了其优势，这些优势随着复杂性的增加而增加。然而，同时对多种疾病进行时空建模的贡献并不常见。我们提出了一种用于肯尼亚人类免疫缺陷病毒和结核病发病率联合建模的全贝叶斯分层时空方法。使用 2012-2017 年期间的病例报告数据，我们估计了模型参数，并确定了联合的空间模式和时间变化。我们的模型包括特定和共享的空间和时间效应。特定的随机效应允许不同疾病的共享模式出现偏差。时空交互项描述了每个时间波动下的潜在空间模式。我们假设共享随机效应为结构效应，疾病特定随机效应为非结构效应。我们检测到在肯尼亚西部、中部和南部地区周围约 29 个县中结核病和人类免疫缺陷病毒分布的空间相似性。共享相对风险的分布与人类免疫缺陷病毒疾病特异性相对风险差异最小，而结核病的分布则有更多的县被认为是高风险地区。贝叶斯分层模型的灵活性和信息丰富的输出使我们能够识别与每种疾病相关的相对风险分布的异同。估计人类免疫缺陷病毒和结核病的共享相对风险为疾病的协作监测和控制工作提供了额外的见解。
