Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the state-of-the-art approach for disease mapping since they are able to both capture structure in the data and robustly characterise uncertainty. When working with areal data, e.g.~aggregates at the administrative unit level such as district or province, current models rely on the adjacency structure of areal units to account for spatial correlations and perform shrinkage. The goal of disease surveillance systems is to track disease outcomes over time. This task is especially challenging in crisis situations which often lead to redrawn administrative boundaries, meaning that data collected before and after the crisis are no longer directly comparable. Moreover, the adjacency-based approach ignores the continuous nature of spatial processes and cannot solve the change-of-support problem, i.e.~when estimates are required to be produced at different administrative levels or levels of aggregation. We present a novel, practical, and easy to implement solution to solve these problems 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 by encoding aggregates over administrative units. We map malaria prevalence in Kenya, a country in which administrative boundaries changed in 2010.

翻译：基于模型的疾病制图仍是公共卫生与疾病监测领域中支撑政策制定的基础工具。分层贝叶斯模型因其既能捕捉数据结构又能稳健刻画不确定性的能力，已成为疾病制图领域的前沿方法。在处理区域数据（例如区、省等行政单元层面的聚合数据）时，现有模型依赖区域单元的邻接结构来表征空间相关性并执行收缩。疾病监测系统的目标是追踪疾病结局随时间的变化趋势。这一任务在危机情境下尤为困难——危机常导致行政边界重划，使得危机前后收集的数据失去直接可比性。此外，基于邻接的方法忽略了空间过程的连续性，且无法解决支持度转换问题（即当需要在不同行政层级或聚合层级生成估计值时）。我们提出一种新颖、实用且易于实施的解决方案，该方法结合深度生成建模与全贝叶斯推断：基于近期提出的PriorVAE方法（该方法通过编码行政单元的聚合数据，利用变分自编码器对小区域施加空间先验），我们绘制了肯尼亚（该国行政边界于2010年发生变更）的疟疾患病率地图。