JSTMapp: A web-based joint spatiotemporal modelling and mapping application for epidemiologists. [PDF]
PLoS ONEDisease mapping models help create disease risk maps, which public health policymakers can use to design disease control and monitoring programmes.
Alfred Ngwira +3 more
doaj +2 more sources
Addressing spatial confounding in geostatistical regression models: An R‐INLA approach
Methods in Ecology and EvolutionSpatial confounding, which has been studied extensively in recent years, can explain inconsistencies between results obtained by regression models with and without spatial modelling.
Jérémy Lamouroux +4 more
doaj +4 more sources
Revista Latinoamericana de Población, 2020 El trabajo busca modelar la distribución de la tasa de incidencia acumulada de COVID-19 en los municipios de México a través del ajuste de tres modelos lineales generalizados (en competencia) con efectos espaciales y temporales y función de enlace ...
Gerardo Núñez Medina
doaj +12 more sources
Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation
Mathematics, 2021 The integrated nested Laplace approximation (INLA) provides a fast and effective method for marginal inference in Bayesian hierarchical models. This methodology has been implemented in the R-INLA package which permits INLA to be used from within R ...
Virgilio Gómez-Rubio +2 more
doaj +1 more source
Unveiling disparities in lung cancer care: a joint spatio-temporal analysis of multidisciplinary meeting presentation, supportive care screening, and diagnostic timeliness in Victoria [PDF]
BMC MedicineBackground Lung cancer remains the most diagnosed malignancy and the leading cause of cancer-related mortality worldwide. Improving key clinical quality indicators (CQIs), including multidisciplinary meeting (MDM) presentation, supportive care screening,
Getayeneh Antehunegn Tesema +3 more
doaj +2 more sources
New Frontiers in Bayesian Modeling Using the INLA Package in R
Journal of Statistical Software, 2021 The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models.
Janet Van Niekerk +3 more
doaj +1 more source
Laplace approximation for conditional autoregressive models for spatial data of diseases
MethodsX, 2022 Conditional autoregressive (CAR) distributions are used to account for spatial autocorrelation in small areal or lattice data to assess the spatial risks of diseases.
Guiming Wang
doaj +1 more source
Bayesian Multivariate Spatial Models for Lattice Data with INLA
Journal of Statistical Software, 2021 The INLAMSM package for the R programming language provides a collection of multivariate spatial models for lattice data that can be used with the INLA package for Bayesian inference.
Francisco Palmí-Perales +2 more
doaj +1 more source
Bayesian Model Averaging with the Integrated Nested Laplace Approximation
Econometrics, 2020 The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent ...
Virgilio Gómez-Rubio +2 more
doaj +1 more source
INLAjoint: Multivariate Joint Modeling for Longitudinal and Time-to-Event Outcomes with 'INLA' [PDF]
, 2023 Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA).
Van Niekerk, Janet +3 more
core +1 more source