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.
- Version0.0.2
- R versionR (≥ 3.5.0)
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release12/08/2024
Team
Amin Abed
MaintainerShow author detailsZeinab Mashreghi
Show author detailsRolesThesis advisorMahmoud Torabi
Show author detailsRolesThesis advisor
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- Imports3 packages
- Suggests1 package