A simple in-host model for COVID-19 with treatments: model prediction and calibration

• Published in: Journal of mathematical biology Vol. 86; no. 2; p. 20
• Main Authors: Al-Darabsah, Isam, Liao, Kang-Ling, Portet, Stéphanie
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023; Springer Nature B.V
• Subjects: Applications of Mathematics; Calibration; COVID-19; Datasets; Equilibrium; Eradication; Humans; Immune system; Infections; Mathematical and Computational Biology; Mathematics; Mathematics and Statistics; Models, Theoretical; Perturbation; Predictions; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Viral diseases; Viruses; COVID-19; Treatment efficacy; Model calibration; 92B05; 34D20; Infected cells dynamics; 92-10; Mathematical modelling
• ISSN: 0303-6812; 1432-1416
• DOI: 10.1007/s00285-022-01849-6

In this paper, we provide a simple ODEs model with a generic nonlinear incidence rate function and incorporate two treatments, blocking the virus binding and inhibiting the virus replication to investigate the impact of calibration on model predictions for the SARS-CoV-2 infection dynamics. We derive conditions of the infection eradication for the long-term dynamics using the basic reproduction number, and complement the characterization of the dynamics at short-time using the resilience and reactivity of the virus-free equilibrium are considered to inform on the average time of recovery and sensitivity to perturbations in the initial virus free stage. Then, we calibrate the treatment model to clinical datasets for viral load in mild and severe cases and immune cells in severe cases. Based on the analysis, the model calibrated to these different datasets predicts distinct scenarios: eradication with a non reactive virus-free equilibrium, eradication with a reactive virus-free equilibrium, and failure of infection eradication. Moreover, severe cases generate richer dynamics and different outcomes with the same treatment. Calibration to different datasets can lead to diverse model predictions, but combining long- and short-term dynamics indicators allows the categorization of model predictions and determination of infection severity.