- Author
- Michiel Rollier (UGent) , Gisele Helena Barboni Miranda (UGent) , Jenna Vergeynst (UGent) , Joris Meys (UGent) , Tijs Alleman (UGent) and Jan Baetens (UGent)
- Organization
- Project
- Abstract
- We analyse and mutually compare time series of covid-19-related data and mobility data across Belgium's 43 arrondissements (NUTS 3). In this way, we reach three conclusions. First, we could detect a decrease in mobility during high-incidence stages of the pandemic. This is expressed as a sizeable change in the average amount of time spent outside one's home arrondissement, investigated over five distinct periods, and in more detail using an inter-arrondissement "connectivity index"(CI). Second, we analyse spatio-temporal covid-19-related hospitalisation time series, after smoothing them using a generalise additive mixed model (GAMM). We confirm that some arrondissements are ahead of others and morphologically dissimilar to others, in terms of epidemiological progression. The tools used to quantify this are time-lagged cross-correlation (TLCC) and dynamic time warping (DTW), respectively. Third, we demonstrate that an arrondissement's CI with one of the three identified first-outbreak arrondissements is correlated to a substantial local excess mortality some five to six weeks after the first outbreak. More generally, we couple results leading to the first and second conclusion, in order to demonstrate an overall correlation between CI values on the one hand, and TLCC and DTW values on the other. We conclude that there is a strong correlation between physical movement of people and viral spread in the early stage of the sars-cov-2 epidemic in Belgium, though its strength weakens as the virus spreads.
- Keywords
- covid-19, Epidemiology, Mobility, Time series analysis, Generalised additive mixed model, TIME, EMERGENCE, DISEASE, IMPACT
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01H11D71PH2FEVBC0X4232Q7BJ
- MLA
- Rollier, Michiel, et al. “Mobility and the Spatial Spread of SARS-COV-2 in Belgium.” MATHEMATICAL BIOSCIENCES, vol. 360, 2023, doi:10.1016/j.mbs.2022.108957.
- APA
- Rollier, M., Barboni Miranda, G. H., Vergeynst, J., Meys, J., Alleman, T., & Baetens, J. (2023). Mobility and the spatial spread of SARS-COV-2 in Belgium. MATHEMATICAL BIOSCIENCES, 360. https://doi.org/10.1016/j.mbs.2022.108957
- Chicago author-date
- Rollier, Michiel, Gisele Helena Barboni Miranda, Jenna Vergeynst, Joris Meys, Tijs Alleman, and Jan Baetens. 2023. “Mobility and the Spatial Spread of SARS-COV-2 in Belgium.” MATHEMATICAL BIOSCIENCES 360. https://doi.org/10.1016/j.mbs.2022.108957.
- Chicago author-date (all authors)
- Rollier, Michiel, Gisele Helena Barboni Miranda, Jenna Vergeynst, Joris Meys, Tijs Alleman, and Jan Baetens. 2023. “Mobility and the Spatial Spread of SARS-COV-2 in Belgium.” MATHEMATICAL BIOSCIENCES 360. doi:10.1016/j.mbs.2022.108957.
- Vancouver
- 1.Rollier M, Barboni Miranda GH, Vergeynst J, Meys J, Alleman T, Baetens J. Mobility and the spatial spread of SARS-COV-2 in Belgium. MATHEMATICAL BIOSCIENCES. 2023;360.
- IEEE
- [1]M. Rollier, G. H. Barboni Miranda, J. Vergeynst, J. Meys, T. Alleman, and J. Baetens, “Mobility and the spatial spread of SARS-COV-2 in Belgium,” MATHEMATICAL BIOSCIENCES, vol. 360, 2023.
@article{01H11D71PH2FEVBC0X4232Q7BJ, abstract = {{We analyse and mutually compare time series of covid-19-related data and mobility data across Belgium's 43 arrondissements (NUTS 3). In this way, we reach three conclusions. First, we could detect a decrease in mobility during high-incidence stages of the pandemic. This is expressed as a sizeable change in the average amount of time spent outside one's home arrondissement, investigated over five distinct periods, and in more detail using an inter-arrondissement "connectivity index"(CI). Second, we analyse spatio-temporal covid-19-related hospitalisation time series, after smoothing them using a generalise additive mixed model (GAMM). We confirm that some arrondissements are ahead of others and morphologically dissimilar to others, in terms of epidemiological progression. The tools used to quantify this are time-lagged cross-correlation (TLCC) and dynamic time warping (DTW), respectively. Third, we demonstrate that an arrondissement's CI with one of the three identified first-outbreak arrondissements is correlated to a substantial local excess mortality some five to six weeks after the first outbreak. More generally, we couple results leading to the first and second conclusion, in order to demonstrate an overall correlation between CI values on the one hand, and TLCC and DTW values on the other. We conclude that there is a strong correlation between physical movement of people and viral spread in the early stage of the sars-cov-2 epidemic in Belgium, though its strength weakens as the virus spreads.}}, articleno = {{108957}}, author = {{Rollier, Michiel and Barboni Miranda, Gisele Helena and Vergeynst, Jenna and Meys, Joris and Alleman, Tijs and Baetens, Jan}}, issn = {{0025-5564}}, journal = {{MATHEMATICAL BIOSCIENCES}}, keywords = {{covid-19,Epidemiology,Mobility,Time series analysis,Generalised additive mixed model,TIME,EMERGENCE,DISEASE,IMPACT}}, language = {{eng}}, pages = {{12}}, title = {{Mobility and the spatial spread of SARS-COV-2 in Belgium}}, url = {{http://doi.org/10.1016/j.mbs.2022.108957}}, volume = {{360}}, year = {{2023}}, }
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