Parsimonious models of in-host viral dynamics and immune response
Mathematical models of in-host viral dynamics and immune response are a vital tool for patient-specific estimation of the initial viral load, prediction of the course of an infection, etc. The COVID-19 pandemics has given impetus to the development of models with an ever-increasing degree of complex...
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Published in | Applied mathematics letters Vol. 145 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier
07.07.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Mathematical models of in-host viral dynamics and immune response are a vital tool for patient-specific estimation of the initial viral load, prediction of the course of an infection, etc. The COVID-19 pandemics has given impetus to the development of models with an ever-increasing degree of complexity. We show that one of the most popular models---the Target Cell Limited model---fails the identifiability test, i.e., its parameters cannot be uniquely inferred from readily available data such as viral load measurements. Here, we present a model that is both identifiable and parsimonious according to information criteria. Our model's predictions match both reported observations of COVID-19 patients and predictions of its more complex counterparts. |
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Bibliography: | SC0023163 USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) |
ISSN: | 0893-9659 1873-5452 |