Model-based disease mapping remains a fundamental policy-informing tool in public health and disease surveillance. Hierarchical Bayesian models have become the state-of-the-art approach for disease mapping since they are able to capture structure in the data, as well as to characterise uncertainty. When working with areal data, e.g.~aggregates at the administrative unit level such as district or province, routinely used models rely on the adjacency structure of areal units to account for spatial correlations. The goal of disease surveillance systems is to track disease outcomes over time. This task provides challenging in situations of crises, such as political changes, leading to changes of administrative boundaries. Kenya is an example of a country where change of boundaries took place in 2010. Moreover, the adjacency-based approach ignores the continuous nature of spatial processes and cannot solve the change-of-support problem, i.e.~when administrative boundaries change or when estimates must be produced at a different administrative level. We present a novel, practical, and easy to implement solution relying on a methodology combining deep generative modelling and fully Bayesian inference: we build on the recently proposed PriorVAE method able to encode spatial priors over small areas with variational autoencoders, to map malaria prevalence in Kenya.

翻译：基于模型的疾病制图仍是公共卫生和疾病监测领域中一项基础性政策参考工具。分层贝叶斯模型已成为疾病制图的前沿方法，因其既能捕捉数据结构，又能量化不确定性。在处理区域数据（如地区或省份等行政单元层面的聚合数据）时，常规模型依赖行政单元的邻接结构来刻画空间相关性。疾病监测系统的目标是追踪疾病结果随时间的变化。在政治变革等危机情境下，行政边界发生变动，此项任务面临挑战。肯尼亚便是2010年发生边界变更的国家之一。此外，基于邻接的方法忽视了空间过程的连续性，且无法解决支持区域变更问题（即行政边界变动或需在不同行政层级生成估算结果时）。我们提出一种新颖、实用且易于实施的解决方案，该方法结合了深度生成建模与完全贝叶斯推断：基于新近提出的PriorVAE方法（该方法利用变分自编码器对小区域进行空间先验编码），我们绘制了肯尼亚的疟疾患病率地图。