Spatial Modeling of Infectious Disease with Reinfection R package GDILM.SEIRS version 0.0.7 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. The methods are described in Abed, Torabi and Mashreghi (2025) <doi:10.1016/j.sste.2025.100780>. Software Depends: R (>= 3.5.0) Imports: MASS, mvtnorm, ngspatial, stats Suggests: testthat (>= 3.0.0) Amin Abed <abeda@myumanitoba.ca> Comprehensive R Archive Network (CRAN) Amin Abed [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-7381-4721>), Mahmoud Torabi [ths], Zeinab Mashreghi [ths] MIT + file LICENSE (https://CRAN.R-project.org/package=GDILM.SEIRS/LICENSE) 2026-09-06 application/tgz https://CRAN.R-project.org/package=GDILM.SEIRS doi:10.32614/CRAN.package.GDILM.SEIRS