A Mobility-Driven Spatially Explicit SEIQRD COVID-19 Model with VOCs, seasonality, and vaccines

In this work we extend our previously developed compartmental SEIQRD model for SARS-CoV-2 in Belgium. The model is geographically stratified into eleven provinces and a telecommunication dataset provided by Belgium's biggest operator is used to incorporate interprovincial mobility. We introduce variants, seasonality, and vaccines in our model, as their addition has proven critical for the description, forecasting and understanding of the COVID-19 pandemic in Belgium. We then calibrate the model using the daily number of hospitalisations in each province and serological data. We demonstrate how our model can be used to set up hypothetical scenarios to study the combined impacts of new variants, an ongoing nation-wide vaccination campaign and social relaxations. In this way, our model can be used to provide policymakers with relevant insights on the optimal timing of the release of social restrictions. We finally discuss the impact of locally altering social contact and mobility on shielding or containing epidemics and find that lowering social contact is more efficient than lowering mobility to tame a SARS-CoV-2 epidemic.