Modeling Challenges of Ebola Virus-Host Dynamics during Infection and Treatment.

Viruses

The Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Loyola University Medical Center, Maywood, IL 60153, USA.

Published: January 2020

AI Article Synopsis

  • Mathematical modeling of Ebola virus dynamics is underdeveloped due to a lack of data on viral kinetics and approved antiviral treatments, leading to oversimplified models
  • Recent models suggest that nearly all liver cells affected by EBOV become immune to infection within seven days without treatment
  • Comparing patient observations with model predictions underscores the complexities of EBOV modeling and the need for continued interdisciplinary research to improve understanding and treatment of Ebola virus disease.

Article Abstract

Mathematical modeling of Ebola virus (EBOV)-host dynamics during infection and treatment in vivo is in its infancy due to few studies with frequent viral kinetic data, lack of approved antiviral therapies, and limited insight into the timing of EBOV infection of cells and tissues throughout the body. Current in-host mathematical models simplify EBOV infection by assuming a single homogeneous compartment of infection. In particular, a recent modeling study assumed the liver as the largest solid organ targeted by EBOV infection and predicted that nearly all cells become refractory to infection within seven days of initial infection without antiviral treatment. We compared our observations of EBOV kinetics in multiple anatomic compartments and hepatocellular injury in a critically ill patient with Ebola virus disease (EVD) with this model's predictions. We also explored the model's predictions, with and without antiviral therapy, by recapitulating the model using published inputs and assumptions. Our findings highlight the challenges of modeling EBOV-host dynamics and therapeutic efficacy and emphasize the need for iterative interdisciplinary efforts to refine mathematical models that might advance understanding of EVD pathogenesis and treatment.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7019702PMC
http://dx.doi.org/10.3390/v12010106DOI Listing

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