A Stochastic Mobility-Driven Spatially Explicit SEIQRD COVID-19 Model with VOCs, Seasonality, and Vaccines
Tijs W. Alleman, Michiel Rollier, Jenna Vergeynst, Jan M. Baetens

TL;DR
This paper presents an extended spatially explicit SEIQRD COVID-19 model incorporating variants, vaccines, seasonality, and mobility data, calibrated with Belgian data, to inform policy decisions on social restrictions and epidemic control.
Contribution
The study introduces a geographically stratified SEIQRD model with variants, vaccines, and seasonality, calibrated with real mobility and hospitalization data for Belgium.
Findings
Model accurately fits hospitalization and serological data.
Interprovincial mobility was not essential for model accuracy.
Reducing social contact is more effective than mobility restrictions.
Abstract
In this work, we extend our previously developed compartmental SEIQRD model for SARS-CoV-2 in Belgium. We introduce SARS-CoV-2 variants of concern, vaccines, and seasonality in our model, as their addition has proven necessary for modelling SARS-CoV-2 transmission dynamics during the 2020-2021 COVID-19 pandemic in Belgium. The model is geographically stratified into eleven spatial patches (provinces), and a telecommunication dataset provided by Belgium's biggest operator is used to incorporate interprovincial mobility. We calibrate the model using the daily number of hospitalisations in each province and serological data. We find the model adequately describes these data, but the addition of interprovincial mobility was not necessary to obtain an accurate description of the 2020-2021 SARS-CoV-2 pandemic in Belgium. We further demonstrate how our model can be used to help policymakers…
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Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Long-Term Effects of COVID-19
