2020/06/25 by Gemma Massonis, Julio R. Banga, Massonis, Gemma +3 · 3 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #COVID-19 epidemiological studies #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM) #SARS-CoV-2 and COVID-19 Research
paper · pdf · doi:10.48550/arxiv.2006.14295
openalex publication_date 2020/06/25 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
The recent coronavirus disease (COVID-19) outbreak has dramatically increased\nthe public awareness and appreciation of the utility of dynamic models. At the\nsame time, the dissemination of contradictory model predictions has highlighted\ntheir limitations. If some parameters and/or state variables of a model cannot\nbe determined from output measurements, its ability to yield correct insights\n-- as well as the possibility of controlling the system -- may be compromised.\nEpidemic dynamics are commonly analysed using compartmental models, and many\nvariations of such models have been used for analysing and predicting the\nevolution of the COVID-19 pandemic. In this paper we survey the different\nmodels proposed in the literature, assembling a list of 36 model structures and\nassessing their ability to provide reliable information. We address the problem\nusing the control theoretic concepts of structural identifiability and\nobservability. Since some parameters can vary during the course of an epidemic,\nwe consider both the constant and time-varying parameter assumptions. We\nanalyse the structural identifiability and observability of all of the models,\nconsidering all plausible choices of outputs and time-varying parameters, which\nleads us to analyse 255 different model versions. We classify the models\naccording to their structural identifiability and observability under the\ndifferent assumptions and discuss the implications of the results. We also\nillustrate with an example several alternative ways of remedying the lack of\nobservability of a model. Our analyses provide guidelines for choosing the most\ninformative model for each purpose, taking into account the available knowledge\nand measurements.\n