GDILM.SEIRS: Spatial Modeling of Infectious Disease with Reinfection (original) (raw)
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: | 0.0.5 |
|---|---|
| Depends: | R (≥ 3.5.0) |
| Imports: | MASS, mvtnorm, ngspatial, stats |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2025-11-02 |
| DOI: | 10.32614/CRAN.package.GDILM.SEIRS |
| Author: | Amin Abed  [aut, cre, cph], Mahmoud Torabi [ths], Zeinab Mashreghi [ths] |
| Maintainer: | Amin Abed |
| License: | MIT + file |
| NeedsCompilation: | no |
| CRAN checks: | GDILM.SEIRS results [issues need fixing before 2025-11-21] |
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