A mechanistic spatio‐temporal modeling of COVID‐19 data

DOI10.1002/bimj.202100318MaRDI QIDQ6149264

Unnamed Author, Unnamed Author, Unnamed Author, Jorge Mateu

Publication date: 4 March 2024

Published in: Biometrical Journal (Search for Journal in Brave)

zbMATH Keywords

spatio-temporal modelsmechanistic modelsCOVID-19inhomogeneous point processesfirst-order intensity function

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Unnamed Item
• Generation interval contraction and epidemic data analysis
• Approximate methods in Bayesian point process spatial models
• Bayesian image restoration, with two applications in spatial statistics (with discussion)
• An introduction to the theory of point processes
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• ggplot2
• Applied Spatial Data Analysis with R
• Handbook of Markov Chain Monte Carlo
• Partial-Likelihood Analysis of Spatio-Temporal Point-Process Data
• Partial likelihood
• Log Gaussian Cox Processes
• A Bayesian Spatial Analysis of the Heterogeneity in Human Mobility Changes During the First Wave of the COVID-19 Epidemic in the United States
• Finite Mixture Models for Mapping Spatially Dependent Disease Counts
• Spatio-temporal point processes, partial likelihood, foot and mouth disease
• A Simplex Method for Function Minimization