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  "Package": "GDILM.SEIRS",
  "Title": "Spatial Modeling of Infectious Disease with Reinfection",
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  "Description": "Geographically Dependent Individual Level Models (GDILMs)\nwithin the Susceptible-Exposed-Infectious-Recovered-Susceptible\n(SEIRS) framework are applied to model infectious disease\ntransmission, incorporating reinfection dynamics. This package\nemploys a likelihood based Monte Carlo Expectation Conditional\nMaximization (MCECM) algorithm for estimating model parameters.\nIt also provides tools for GDILM fitting, parameter estimation,\nAIC calculation on real pandemic data, and simulation studies\ncustomized to user-defined model settings.",
  "URL": "https://doi.org/10.1016/j.sste.2025.100780",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "RoxygenNote": "7.3.2",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-20 13:45:32 UTC",
    "User": "root"
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  "Author": "Amin Abed [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-7381-4721>), Mahmoud Torabi [ths],\nZeinab Mashreghi [ths]",
  "Maintainer": "Amin Abed <abeda@myumanitoba.ca>",
  "Repository": "https://cranhaven.r-universe.dev",
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      "title": "GDILM SEIRS for Real Data",
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