On the occasion of the Spatial Statistics’ 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analyzing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.

值空间统计十周年之际，回顾贝叶斯疾病制图的前世今生，展望未来。我专注于模型的一些关键发展，以及多元和自适应高斯马尔可夫随机场的最新演变及其在疾病映射中的影响和重要性。我将贝叶斯疾病制图作为空间统计的主题进行反思，该主题迄今为止在范围和复杂性方面都取得了进展，并且随着当代健康科学研究（例如空间流行病学、人口和公共卫生）对分析工具的需求不断增加而不断增长，以及药物。我说明了一些疾病绘图模型和方法的（潜在）效用和影响，这些模型和方法用于分析和监测传染病，例如持续大流行期间的 COVID-19 感染风险。