A. Adin Urtasun, E. Orozco-Acosta, M. D. Ugarte
Research in spatio-temporal disease mapping has been very fruitful in recent decades and numerous statistical models have been propose to study the geographic distribution of a disease and its evolution in time. However, the scalability of these models has not been studied in depth yet. In this work, we propose a “divide-and-conquer” based methodology as an alternative modelling approach to the commonly used disease mapping models in order to analyze high-dimensional spatio-temporal data. The methods and algorithms proposed in this work are implemented in the open-source R package "bigDM" (https://github.com/spatialstatisticsupna/bigDM), which allows the user to adapt the modelling scheme to their own processing architecture by performing both parallel and/or distributed computation strategies. Currently, we are working on the development of scalable ecological regression models taking into account the spatial and/or spatio-temporal confounding issues between fixed and random effects.
Palabras clave: Disease mapping, Hierarchical models, Laplace approximations, Massive data, Scalable modelling
Programado
GT19 Estadística Espacio Temporal
7 de junio de 2022 12:00
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