{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T14:30:52Z","timestamp":1792420252099,"version":"4.5.0"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T00:00:00Z","timestamp":1743206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSF","award":["2125246"],"award-info":[{"award-number":["2125246"]}]},{"name":"NSF","award":["2026797"],"award-info":[{"award-number":["2026797"]}]},{"name":"NSF","award":["1R01GM152743-01"],"award-info":[{"award-number":["1R01GM152743-01"]}]},{"name":"ANR-22-CE48-0008 OCCAM project","award":["2125246"],"award-info":[{"award-number":["2125246"]}]},{"name":"ANR-22-CE48-0008 OCCAM project","award":["2026797"],"award-info":[{"award-number":["2026797"]}]},{"name":"ANR-22-CE48-0008 OCCAM project","award":["1R01GM152743-01"],"award-info":[{"award-number":["1R01GM152743-01"]}]},{"name":"NIH NIGMS","award":["2125246"],"award-info":[{"award-number":["2125246"]}]},{"name":"NIH NIGMS","award":["2026797"],"award-info":[{"award-number":["2026797"]}]},{"name":"NIH NIGMS","award":["1R01GM152743-01"],"award-info":[{"award-number":["1R01GM152743-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>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\u2019 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 StructuralIdentifiability.jl package in JULIA. We validated the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB Toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID-19, and Ebola. Additionally, we assessed practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real-world challenges and their role in informing public health interventions.<\/jats:p>","DOI":"10.3390\/v17040496","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T05:21:04Z","timestamp":1743398464000},"page":"496","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting"],"prefix":"10.3390","volume":"17","author":[{"given":"Yuganthi R.","family":"Liyanage","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2194-2251","authenticated-orcid":false,"given":"Gerardo","family":"Chowell","sequence":"additional","affiliation":[{"name":"School of Public Health, Georgia State University, Atlanta, GA 30303, USA"},{"name":"Department of Applied Mathematics, Kyung Hee University, Yongin 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5731-8242","authenticated-orcid":false,"given":"Gleb","family":"Pogudin","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique, CNRS, Ecole Polytechnique, IP Paris, 91120 Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3168-4110","authenticated-orcid":false,"given":"Necibe","family":"Tuncer","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1007\/s11538-008-9323-4","article-title":"Modeling and estimation of kinetic parameters and replicative fitness of HIV-1 from flow-cytometry-based growth competition experiments","volume":"70","author":"Miao","year":"2008","journal-title":"Bull. 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