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A stochastic mobility-driven spatially explicit SEIQRD COVID-19 model with VOCs, seasonality, and vaccines

Tijs Alleman (UGent) , Michiel Rollier (UGent) , Jenna Vergeynst (UGent) and Jan Baetens (UGent)
(2023) APPLIED MATHEMATICAL MODELLING. 123. p.507-525
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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 decide on the optimal timing of the release of social restrictions. We find that adding spatial heterogeneity by geographically stratifying the model results in more uncertain model projections as compared to an equivalent nation-level model, which has both communicative advantages and disadvantages. We finally discuss the impact of imposing local mobility or social contact restrictions to contain an epidemic in a given province and find that lowering social contact is a more effective strategy than lowering mobility.
Keywords
Applied Mathematics, Modeling and Simulation, Policy making, Mobility, Spatially explicit, Compartmental model, Transmission dynamics

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Citation

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MLA
Alleman, Tijs, et al. “A Stochastic Mobility-Driven Spatially Explicit SEIQRD COVID-19 Model with VOCs, Seasonality, and Vaccines.” APPLIED MATHEMATICAL MODELLING, vol. 123, 2023, pp. 507–25, doi:10.1016/j.apm.2023.06.027.
APA
Alleman, T., Rollier, M., Vergeynst, J., & Baetens, J. (2023). A stochastic mobility-driven spatially explicit SEIQRD COVID-19 model with VOCs, seasonality, and vaccines. APPLIED MATHEMATICAL MODELLING, 123, 507–525. https://doi.org/10.1016/j.apm.2023.06.027
Chicago author-date
Alleman, Tijs, Michiel Rollier, Jenna Vergeynst, and Jan Baetens. 2023. “A Stochastic Mobility-Driven Spatially Explicit SEIQRD COVID-19 Model with VOCs, Seasonality, and Vaccines.” APPLIED MATHEMATICAL MODELLING 123: 507–25. https://doi.org/10.1016/j.apm.2023.06.027.
Chicago author-date (all authors)
Alleman, Tijs, Michiel Rollier, Jenna Vergeynst, and Jan Baetens. 2023. “A Stochastic Mobility-Driven Spatially Explicit SEIQRD COVID-19 Model with VOCs, Seasonality, and Vaccines.” APPLIED MATHEMATICAL MODELLING 123: 507–525. doi:10.1016/j.apm.2023.06.027.
Vancouver
1.
Alleman T, Rollier M, Vergeynst J, Baetens J. A stochastic mobility-driven spatially explicit SEIQRD COVID-19 model with VOCs, seasonality, and vaccines. APPLIED MATHEMATICAL MODELLING. 2023;123:507–25.
IEEE
[1]
T. Alleman, M. Rollier, J. Vergeynst, and J. Baetens, “A stochastic mobility-driven spatially explicit SEIQRD COVID-19 model with VOCs, seasonality, and vaccines,” APPLIED MATHEMATICAL MODELLING, vol. 123, pp. 507–525, 2023.
@article{01H6XMJ3N01EDZMAGX96A7ZTZ6,
  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 decide on the optimal timing of the release of social restrictions. We find that adding spatial heterogeneity by geographically stratifying the model results in more uncertain model projections as compared to an equivalent nation-level model, which has both communicative advantages and disadvantages. We finally discuss the impact of imposing local mobility or social contact restrictions to contain an epidemic in a given province and find that lowering social contact is a more effective strategy than lowering mobility.}},
  author       = {{Alleman, Tijs and Rollier, Michiel and Vergeynst, Jenna and Baetens, Jan}},
  issn         = {{0307-904X}},
  journal      = {{APPLIED MATHEMATICAL MODELLING}},
  keywords     = {{Applied Mathematics,Modeling and Simulation,Policy making,Mobility,Spatially explicit,Compartmental model,Transmission dynamics}},
  language     = {{eng}},
  pages        = {{507--525}},
  title        = {{A stochastic mobility-driven spatially explicit SEIQRD COVID-19 model with VOCs, seasonality, and vaccines}},
  url          = {{http://doi.org/10.1016/j.apm.2023.06.027}},
  volume       = {{123}},
  year         = {{2023}},
}
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