Disease mapping is an area of research that has evolved tremendously in the las two decades with the improvements of data registries, computers and software. 
There have been different lines of research in this area. Namely a methodological approach with the proposal of new models to analyse space-time data and their extensions to a multivariate framework; the development of new estimation techniques and software to fit the models, and the estimation of fixed effects in spatial and spatio-temporal models overcoming the issue of spatial confounding.  Additionally, there is a corpus of applied research with direct implications in society. This includes the study of incidence and/or mortality of chronic diseases or violence against women. More precisely, there is now abundant research on  incidence and/or mortality of chronic diseases such as cancer or ischemic heart diseases. Other problems that are now in the social agenda and have been approached in the disease mapping literature include mental disorders such as psychosis or schizophrenia, suicide rates or different forms of crimes against women.
Despite of the research, there are still some challenges. Analysing extensive regions with a large number of small areas requires new scalable methods that produce a more adaptive smoothing in space and time. Additionally, including covariates to the model taking advantage of available information is a must.  In this line, distributional regression can be an interesting methodology that has not be explored in disease mapping. One of the approaches of distributional regression is to model, conditioned on a set of covariates, not only the mean but also the variance. By covariates, we understand not only continuous or categorical covariates but also other less standard forms of covariates such as coordinates of the observation point (with spatially georeferenced observations), discrete spatial information in the form of province, municipality, census tract, or postal code, or random effects defining groups of observations. Distributional regression might also offer an alternative to existent methods for mitigating spatial confounding. 
The purpose of this session is to bring together researchers from two different areas, disease mapping and distributional regression to explore new horizons and possibilities in research, both methodological and practical, in the area of disease mapping.