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:	0.0.6
Depends:	R (≥ 3.5.0)
Imports:	MASS, mvtnorm, ngspatial, stats
Suggests:	testthat (≥ 3.0.0)
Published:	2026-02-13
DOI:	10.32614/CRAN.package.GDILM.SEIRS
Author:	Amin Abed [aut, cre, cph], Mahmoud Torabi [ths], Zeinab Mashreghi [ths]
Maintainer:	Amin Abed <abeda at myumanitoba.ca>
License:	MIT + file LICENSE
URL:	https://doi.org/10.1016/j.sste.2025.100780
NeedsCompilation:	no
CRAN checks:	GDILM.SEIRS results

Documentation:

Reference manual:

GDILM.SEIRS.html , GDILM.SEIRS.pdf

Downloads:

Package source:	GDILM.SEIRS_0.0.6.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available
Old sources:	GDILM.SEIRS archive

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