GDILM.SEIRS: Spatial Modeling of Infectious Disease with Reinfection

Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings.

Version: 0.0.2
Depends: R (≥ 3.5.0)
Imports: MASS, mvtnorm, ngspatial, stats
Suggests: testthat (≥ 3.0.0)
Published: 2024-12-08
DOI: 10.32614/CRAN.package.GDILM.SEIRS
Author: Amin Abed ORCID iD [aut, cre, cph], Mahmoud Torabi [ths], Zeinab Mashreghi [ths]
Maintainer: Amin Abed <abeda at myumanitoba.ca>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: GDILM.SEIRS results [issues need fixing before 2025-01-16]

Documentation:

Reference manual: GDILM.SEIRS.pdf

Downloads:

Package source: GDILM.SEIRS_0.0.2.tar.gz
Windows binaries: r-devel: GDILM.SEIRS_0.0.2.zip, r-release: GDILM.SEIRS_0.0.2.zip, r-oldrel: GDILM.SEIRS_0.0.2.zip
macOS binaries: r-release (arm64): GDILM.SEIRS_0.0.2.tgz, r-oldrel (arm64): GDILM.SEIRS_0.0.2.tgz, r-release (x86_64): GDILM.SEIRS_0.0.2.tgz, r-oldrel (x86_64): GDILM.SEIRS_0.0.2.tgz
Old sources: GDILM.SEIRS archive

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