COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps

[Display omitted] •Proposed dynamic transmission rate with a control rate for COVID-19 modelling.•Modified an “exponential decay” model to capture subexponential growth dynamics.•Improved performance in model fitting and prediction based on six EU countries’ data.•Explored how to associate the global mobility trend with control rate in the model.•Positive correlation found within average change of mobility trend and control rate. Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.