Abstract

In this work, a mathematical Covid-19 epidemic transmission network model is investigated for the study of the virus spread dynamics. The purpose is to present a sensitivity-based estimability analysis as well as an accurate parameter identification approaches for reliable mathematical modeling. The most sensitive parameters of the model are identified using a local sensitivity approach, these sensitivities are then used within an orthogonalization algorithm to assess the estimability of the unknown parameters from available data. A database of newly reported infected and recovered people in China is used. The most estimable model parameters are identified, their accuracy is assessed by computing confidence intervals and their numerical values are also used to compare the model predictions to real data. The Pearson Product-Moment coefficient is computed. Its high values show the accuracy of the new model parameterization and the importance of the used sensitivity-based estimability approach.