{
    "abstract": "Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters' structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology: the generalized growth model (GGM), the generalized logistic model (GLM), the Richards model, the generalized Richards model (GRM), the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the ",
    "authors": [
        {
            "affiliation": "Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA.",
            "firstname": "Yuganthi R",
            "initials": "YR",
            "lastname": "Liyanage"
        },
        {
            "affiliation": "School of Public Health, Georgia State University, Atlanta, GA 30303, USA.\nDepartment of Applied Mathematics, Kyung Hee University, Yongin 17104, Republic of Korea.",
            "firstname": "Gerardo",
            "initials": "G",
            "lastname": "Chowell"
        },
        {
            "affiliation": "Laboratoire d'Informatique, CNRS, Ecole Polytechnique, IP Paris, 91120 Palaiseau, France.",
            "firstname": "Gleb",
            "initials": "G",
            "lastname": "Pogudin"
        },
        {
            "affiliation": "Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA.",
            "firstname": "Necibe",
            "initials": "N",
            "lastname": "Tuncer"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.3390/v17040496\n10.1007/s11538-008-9323-4\n10.1007/s11538-016-0200-2\n10.1016/j.jtbi.2012.12.021\n10.1016/j.jtbi.2021.110998\n10.1016/j.plrev.2024.10.011\n10.1016/j.epidem.2016.01.002\n10.1007/s00285-003-0261-3\n10.1016/S0025-5564(01)00079-7\n10.1137/090757009\n10.3390/math12182837\n10.1016/j.mbs.2018.02.004\n10.1038/s41598-024-51852-8\n10.1016/0025-5564(70)90132-X\n10.1186/s12976-018-0097-6\n10.1137/20M1353289\n10.1016/j.arcontrol.2020.12.001\n10.1080/17513758.2022.2078899\n10.1126/sciadv.abg5234\n10.1007/s00285-021-01711-1\n10.1016/j.mbs.2004.10.011\n10.1007/s00285-023-02007-2\n10.1007/s11538-018-0453-z\n10.1007/s00200-021-00486-8\n10.1007/s11538-007-9279-9\n10.1016/j.coisb.2021.03.005\n10.1093/bioinformatics/btu006",
    "journal": "Viruses",
    "keywords": [
        "epidemic forecasting",
        "phenomenological growth models",
        "practical identifiability",
        "structural identifiability"
    ],
    "methods": null,
    "publication_date": "2025-04-26",
    "pubmed_id": "40284940\n18648886\n27651156\n23333764\n34973274\n39488136\n27266847\n15549308\n11595254\n21785515\n29477671\n38238407\n30642334\n36338855\n33362427\n35635313\n35044820\n34982260\n15854675\n37921877\n29948883\n18247096\n24463185",
    "results": null,
    "title": "Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting.",
    "xml": "<Element 'PubmedArticle' at 0x788506b0cd10>"
}