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.

Keywords: Disease mapping, Hierarchical models, Laplace approximations, Massive data, Scalable modelling

Scheduled

GT19 Time Space Statistics
June 7, 2022  12:00 PM
Conference hall


Other papers in the same session

Spatial Cox Processes in an Infinite-Dimensional Framework

M. P. Frías Bustamante, A. Torres Signes, M. D. Ruiz Medina, J. Mateu Mahiques

Identification of Patterns for Space-Time Event Networks

M. P. Bohorquez Castañeda, A. M. Forero Sanabria, J. Mateu Mahiques, R. R. Renteria Ramos

Statistical inference on intensity functions with applications

M. I. Borrajo García, W. González Manteiga, M. D. Martínez Miranda


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