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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. The methods are described in Abed, Torabi and Mashreghi (2025) <doi:10.1016/j.sste.2025.100780>.
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Package details |
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| Author | Amin Abed [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-7381-4721>), Mahmoud Torabi [ths], Zeinab Mashreghi [ths] |
| Maintainer | Amin Abed <abeda@myumanitoba.ca> |
| License | MIT + file LICENSE |
| Version | 0.0.7 |
| URL | https://doi.org/10.1016/j.sste.2025.100780 |
| Package repository | View on CRAN |
| Installation |
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