Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies.

中文翻译：

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空间流行病学贝叶斯时空模型综述

空间流行病学研究空间和时间上健康结果的模式和决定因素。在这一领域，贝叶斯时空模型因其能够整合空间和时间依赖性、不确定性和复杂相互作用的能力而受到欢迎。然而，与贝叶斯时空模型相关的建模和计算的复杂性因不同疾病而异。目前，对贝叶斯时空模型及其在流行病学中的应用的全面概述有限。本文旨在通过彻底的审查来解决这一差距。本文首先深入研究了有关疾病绘图、预测和回归分析的贝叶斯时空模型的历史发展。随后，文章从时空数据分布、一般时空数据模型、环境协变量、参数估计方法和模型拟合标准等方面对这些模型进行了比较。接下来，概述了基本的准备过程，包括数据采集、数据预处理和可用的统计软件。文章进一步对贝叶斯时空模型在空间流行病学中的应用进行了分类和总结。最后，对这些模型的优缺点及其应用进行了严格的审查。这项综合综述旨在加强对流行病动态时空分布和预测的理解。通过促进有效的疾病审查，特别是在全球 COVID-19 大流行的背景下，该审查具有重要的学术价值和实用价值。它还旨在为制定改进的生态和流行病学预防和控制战略做出贡献。