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Insights into the dynamics and control of COVID-19 infection rates

Lookup NU author(s): Dr Mark Willis, Oscar Prado-Rubio, Dr Moritz von Stosch

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 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.


Publication metadata

Author(s): Willis MJ, Díaz VHG, Prado-Rubio OA, von Stosch M

Publication type: Article

Publication status: Published

Journal: Chaos, Solitons & Fractals

Year: 2020

Volume: 138

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

Publisher: Elsevier

URL: https://doi.org/10.1016/j.chaos.2020.109937

DOI: 10.1016/j.chaos.2020.109937


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