Results 51 to 60 of about 4,738 (165)

Estimating Velocities of Infectious Disease Spread Through Spatio‐Temporal Log‐Gaussian Cox Point Processes

International Statistical Review, EarlyView.
Summary Understanding the spread of infectious diseases such as COVID‐19 is crucial for informed decision‐making and resource allocation. A critical component of disease behaviour is the velocity with which disease spreads, defined as the rate of change between time and space.
Fernando Rodriguez Avellaneda, Jorge Mateu, Paula Moraga   +2 more
wiley   +1 more source

Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique

, 2020
Bayesian spatial models are widely used to analyse data that arise in scientific disciplines such as health, ecology, and the environment. Traditionally, Markov chain Monte Carlo (MCMC) methods have been used to fit these type of models.
Noureen, Shahzeb Raja, Moraga, Paula, Morawiecki, Piotr, Wang, Fengpei, Inoue, Joshua, Dean, Christopher   +5 more
core   +1 more source

Assessing the risk of extreme precipitation in Japan through GEV distribution and spatial modeling

Journal of Hydrology: Regional Studies
Study region: Japan, characterized by diverse climatic zones and complex topography, has experienced increasing frequency and severity of extreme precipitation events in recent decades.
Zhichao Jiao, Jihui Yuan, Craig Farnham, Kazuo Emura   +3 more
doaj   +1 more source

A Bayesian Hierarchical Spatiotemporal Model With Physical Barriers for Extreme Sea‐Level Prediction in Ireland

Environmetrics, Volume 37, Issue 6, September 2026.
ABSTRACT Rising seas raise the vulnerability of coastal regions through higher extreme sea levels. This paper estimates extreme sea levels at gauged and ungauged coastal locations around Ireland and the West Coast of Great Britain, using a Bayesian hierarchical extreme‐value model with spatiotemporal random effects. The annual maxima of tidal residuals
Fernando Mayer, Niamh Mimnagh, Niamh Cahill   +2 more
wiley   +1 more source

sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields

Journal of Statistical Software
Geostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally ...
Sean C. Anderson, Eric J. Ward, Philina A. English, Lewis A. K. Barnett, James T. Thorson   +4 more
doaj   +1 more source

Comparative Analysis of Correlative Modeling Methods in Predicting North American Bird Abundance

Ecology and Evolution, Volume 16, Issue 8, August 2026.
This study compares nine ecological niche model (ENM) methods to predict North American bird abundance and finds positive correlations between environmental suitability and abundance. Results highlight the potential of ENMs for abundance inference, with newer methods showing good performance and demonstrating that simpler data inputs can be effective ...
Jazmín Escobar‐Luján, Fabricio Villalobos, Adolfo G. Navarro‐Sigüenza, Carlos Yañez‐Arenas   +3 more
wiley   +1 more source

A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDEerri [PDF]

, 2017
\ud \ud In this paper a Bayesian geostatistical model is presented for fusion of data obtained at point and areal resolutions. The model is fitted using the INLA and SPDE approaches.
Moraga, Paula, Cramb, S., Marcello Pagano, Cramb, Susanna M., Mengersen, Kerrie L., Pagano, Marcello, Paula Moraga, Moraga, P., Susanna M. Cramb, Mengersen, K., Pagano, M., Kerrie L. Mengersen   +11 more
core   +2 more sources

Data-driven soil salinization mapping: risk prediction and uncertainty quantification based on Bayesian inference

Geoderma
Soil salinization poses a serious global threat to agricultural production and has emerged as a critical issue of land degradation. To comprehensively investigate the risks and uncertainty quantification associated with soil salinization, Yucheng County,
Yujian Yang, Ying Zhao, Rongjiang Yao, Xueqin Tong   +3 more
doaj   +1 more source

Bayesian spatial modelling of malaria burden in two contrasted eco-epidemiological facies in Benin (West Africa): call for localized interventions

BMC Public Health, 2022
Background Despite a global decrease in malaria burden worldwide, malaria remains a major public health concern, especially in Benin children, the most vulnerable group.
Barikissou Georgia Damien, Akoeugnigan Idelphonse Sode, Daniel Bocossa, Emmanuel Elanga-Ndille, Badirou Aguemon, Vincent Corbel, Marie-Claire Henry, Romain Lucas Glèlè Kakaï, Franck Remoué   +8 more
doaj   +1 more source

Hierarchical Bayesian Modeling of Total Column Ozone: Unraveling Equatorial Variability Over Ethiopia Using Satellite Data and Multisource Covariates

Environmetrics, Volume 37, Issue 5, July 2026.
ABSTRACT Understanding the spatiotemporal dynamics of total column ozone (TCO) is critical for monitoring ultraviolet (UV) exposure and ozone trends, particularly in equatorial regions where variability remains underexplored. This study investigates monthly TCO over Ethiopia (2012–2022) using a Bayesian hierarchical model implemented via Integrated ...
Yassin Tesfaw Abebe, Abdu Mohammed Seid, Lassi Roininen, U. Jaya Parakash Raju, Abebaw Bizuneh Alemu   +4 more
wiley   +1 more source