COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions

• Published in: Chemical engineering science Vol. 231; p. 116330
• Main Authors: Willis, Mark J., Wright, Allen, Bramfitt, Victoria, Díaz, Victor Hugo Grisales
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.02.2021
• Subjects: COVID-19; Discrete event modelling and management; Model calibration; Time varying disease transmission rate; COVID-19; Discrete event modelling and management; Model calibration; Time varying disease transmission rate
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2020.116330

•Established chemical engineering modelling practice applied to COVID 19 Re estimation.•Effective reproduction number modelled as time varying stoichiometry in kinetic model.•Model uses piecewise continuous integration and event and discontinuity management.•Nested optimiser algorithm to estimate Re from data with time varying NPIs.•Calibration and estimation of non-linear response in Re to NPIs throughout epidemic. Mathematical models are useful in epidemiology to understand COVID-19 contagion dynamics. We aim to demonstrate the effectiveness of parameter regression methods to calibrate an established epidemiological model describing infection rates subject to active, varying non-pharmaceutical interventions (NPIs). We assess the potential of established chemical engineering modelling principles and practice applied to epidemiological systems. We exploit the sophisticated parameter regression functionality of a commercial chemical engineering simulator with piecewise continuous integration, event and discontinuity management. We develop a strategy for calibrating and validating a model. Our results using historic data from 4 countries provide insights into on-going disease suppression measures, while visualisation of reported data provides up-to-date condition monitoring of the pandemic status. The effective reproduction number response to NPIs is non-linear with variable response rate, magnitude and direction. Our purpose is developing a methodology without presenting a fully optimised model, or attempting to predict future course of the COVID-19 pandemic.