A mechanistic spatio‐temporal modeling of COVID‐19 data-Reference-Cited by-同舟云学术

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A mechanistic spatio‐temporal modeling of COVID‐19 data

Published:2022-08-07 Issue:1 Volume:65 Page:2100318
ISSN:0323-3847
Container-title:Biometrical Journal
language:en
Short-container-title:Biometrical J

Author:

Briz‐Redón Álvaro¹²^ORCID,Iftimi Adina¹,Mateu Jorge³,Romero‐García Carolina⁴⁵^ORCID

Affiliation:

1. Department of Statistics and Operations Research University of Valencia Spain

2. Statistics Office City Council of Valencia Spain

3. Department of Mathematics University Jaume I Spain

4. Department of Anesthesia Critical Care and Pain Unit General University Hospital Spain

5. Division of Research Methodology European University of Valencia Spain

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100318

Reference60 articles.

1. Finite Mixture Models for Mapping Spatially Dependent Disease Counts

2. Spatial Point Patterns

3. A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence

4. Bayesian image restoration, with two applications in spatial statistics

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1. Semi-parametric Spatio-Temporal Hawkes Process for Modelling Road Accidents in Rome;Journal of Agricultural, Biological and Environmental Statistics;2024-04-09

2. Outward looking foreign direct investment and its determinants in highly indebted Eastern African countries;PLOS ONE;2024-02-01

3. Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022;PLOS ONE;2024-02-01

4. A spatio‐temporal Dirichlet process mixture model for coronavirus disease‐19;Statistics in Medicine;2023-10-09

5. Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: a functional data analysis from August 2020 to March 2022;2023-06-22

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