Browse by author
Lookup NU author(s): Dr Mark Willis,
Dr Moritz von Stosch
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2020. This work aims to model, simulate and provide insights into the dynamics and control of COVID-19 infection rates. Using an established epidemiological model augmented with a time-varying disease transmission rate allows daily model calibration using COVID-19 case data from countries around the world. This hybrid model provides predictive forecasts of the cumulative number of infected cases. It also reveals the dynamics associated with disease suppression, demonstrating the time to reduce the effective, time-dependent, reproduction number. Model simulations provide insights into the outcomes of disease suppression measures and the predicted duration of the pandemic. Visualisation of reported data provides up-to-date condition monitoring, while daily model calibration allows for a continued and updated forecast of the current state of the pandemic.
Author(s): Willis MJ, Díaz VHG, Prado-Rubio OA, von Stosch M
Publication type: Article
Publication status: Published
Journal: Chaos, Solitons & Fractals
Print publication date: 01/09/2020
Online publication date: 28/05/2020
Acceptance date: 25/05/2020
Date deposited: 10/06/2020
ISSN (print): 0960-0779
Altmetrics provided by Altmetric