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

Mark J. Willis, Victor Hugo Grisales Díaz, Oscar Andrés Prado-Rubio and Moritz von Stosch

Chaos, Solitons & Fractals, 2020, vol. 138, issue C

Abstract: 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.

Keywords: COVID-19; Model calibration; Predictive modelling and simulation; Time varying disease transmission rate (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303362

DOI: 10.1016/j.chaos.2020.109937

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