A modified SEIR compartmental model is constructed for COVID-19 in a metapopulational setting with fine-scaled population data. The model is stochastically simulated with GLEAMviz that provides realistic short and long distance mobility based on real-world data. A retrospective evaluation in both the temporal and spatial dimensions is conducted with over one year of collected data from the COVID-19 pandemic in Sweden. We find that to reproduce the multimodal behavior a seasonal scaling factor of 0.4 is necessary, which is introduced to the model by scaling R0 with corresponding sinusoidal. For the spatial dimension we divide Sweden into a southern, middle and northern region and the model is able to capture the dynamics in all regions. Additionally, we introduce compartmental models in a constructive manner and motivate metapopulational models, including how commuting is integrated by a force of infection. The next generation method for calculating the basic reproductive number for arbitrary compartmental models is also presented.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-85945 |
| Date | January 2021 |
| Creators | Skogh, Filip |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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