A local-EM algorithm for spatio-temporal disease mapping with aggregated data

This paper extends existing work by incorporating a temporally varying risk surface and an explicit data structure which contains a mixture of point locations and locations aggregated to non-nested areas. This added flexibility allows the modelling of data amalgamated from different sources and collected over many years. While local-EM leads naturally to an Expectation-Maximization-Smoothing algorithm, the extension to mixtures of aggregations leads to a modified algorithm that includes an additive term at every iteration to account for observed point locations.