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A spatial SEIR model for COVID-19 in South Africa

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dc.contributor.author Fabris-Rotelli, I
dc.contributor.author Holloway, Jennifer P
dc.contributor.author Kimmie, Z
dc.contributor.author Archibald, S
dc.contributor.author Debba, Pravesh
dc.contributor.author Manjoo-Docrat, R
dc.contributor.author Le Roux, Alize
dc.contributor.author Dudeni-Tlhone, Nontembeko
dc.contributor.author Van Rensburg, CJ
dc.contributor.author Thiede, R
dc.date.accessioned 2023-01-17T06:49:30Z
dc.date.available 2023-01-17T06:49:30Z
dc.date.issued 2022-11
dc.identifier.citation Fabris-Rotelli, I., Holloway, J.P., Kimmie, Z., Archibald, S., Debba, P., Manjoo-Docrat, R., Le Roux, A. & Dudeni-Tlhone, N. et al. 2022. A spatial SEIR model for COVID-19 in South Africa. <i>Journal of Data Science, Statistics, and Visualisation, vol. 2(7).</i> http://hdl.handle.net/10204/12577 en_ZA
dc.identifier.issn 2773-0689
dc.identifier.uri doi: 10.20944/preprints202106.0262.v1
dc.identifier.uri http://hdl.handle.net/10204/12577
dc.description.abstract The virus SARS-CoV-2 has resulted in numerous modelling approaches arising rapidly to understand the spread of the disease COVID-19 and to plan for future interventions. Herein, we present an SEIR model with a spatial spread component as well as four infectious compartments to account for the variety of symptom levels and transmission rate. The model takes into account the pattern of spatial vulnerability in South Africa through a vulnerability index that is based on socioeconomic and health susceptibility characteristics. Another spatially relevant factor in this context is level of mobility throughout. The thesis of this study is that without the contextual spatial spread modelling, the heterogeneity in COVID-19 prevalence in the South African setting would not be captured. The model is illustrated on South African COVID-19 case counts and hospitalisations. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://jdssv.org/index.php/jdssv/article/view/46 en_US
dc.source Journal of Data Science, Statistics, and Visualisation, vol. 2(7) en_US
dc.subject Covid-19 en_US
dc.subject Excess deaths en_US
dc.subject Hospitilisations en_US
dc.subject SEIR model en_US
dc.title A spatial SEIR model for COVID-19 in South Africa en_US
dc.type Article en_US
dc.description.pages 14-45 en_US
dc.description.note Journal of Data Science, Statistics, and Visualisation is an open access journal. en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.cluster Smart Places en_US
dc.description.impactarea Data Science en_US
dc.description.impactarea ISSR Management Area en_US
dc.identifier.apacitation Fabris-Rotelli, I., Holloway, J. P., Kimmie, Z., Archibald, S., Debba, P., Manjoo-Docrat, R., ... Thiede, R. (2022). A spatial SEIR model for COVID-19 in South Africa. <i>Journal of Data Science, Statistics, and Visualisation, vol. 2(7)</i>, http://hdl.handle.net/10204/12577 en_ZA
dc.identifier.chicagocitation Fabris-Rotelli, I, Jennifer P Holloway, Z Kimmie, S Archibald, Pravesh Debba, R Manjoo-Docrat, Alize Le Roux, Nontembeko Dudeni-Tlhone, CJ Van Rensburg, and R Thiede "A spatial SEIR model for COVID-19 in South Africa." <i>Journal of Data Science, Statistics, and Visualisation, vol. 2(7)</i> (2022) http://hdl.handle.net/10204/12577 en_ZA
dc.identifier.vancouvercitation Fabris-Rotelli I, Holloway JP, Kimmie Z, Archibald S, Debba P, Manjoo-Docrat R, et al. A spatial SEIR model for COVID-19 in South Africa. Journal of Data Science, Statistics, and Visualisation, vol. 2(7). 2022; http://hdl.handle.net/10204/12577. en_ZA
dc.identifier.ris TY - Article AU - Fabris-Rotelli, I AU - Holloway, Jennifer P AU - Kimmie, Z AU - Archibald, S AU - Debba, Pravesh AU - Manjoo-Docrat, R AU - Le Roux, Alize AU - Dudeni-Tlhone, Nontembeko AU - Van Rensburg, CJ AU - Thiede, R AB - The virus SARS-CoV-2 has resulted in numerous modelling approaches arising rapidly to understand the spread of the disease COVID-19 and to plan for future interventions. Herein, we present an SEIR model with a spatial spread component as well as four infectious compartments to account for the variety of symptom levels and transmission rate. The model takes into account the pattern of spatial vulnerability in South Africa through a vulnerability index that is based on socioeconomic and health susceptibility characteristics. Another spatially relevant factor in this context is level of mobility throughout. The thesis of this study is that without the contextual spatial spread modelling, the heterogeneity in COVID-19 prevalence in the South African setting would not be captured. The model is illustrated on South African COVID-19 case counts and hospitalisations. DA - 2022-11 DB - ResearchSpace DP - CSIR J1 - Journal of Data Science, Statistics, and Visualisation, vol. 2(7) KW - Covid-19 KW - Excess deaths KW - Hospitilisations KW - SEIR model LK - https://researchspace.csir.co.za PY - 2022 SM - 2773-0689 T1 - A spatial SEIR model for COVID-19 in South Africa TI - A spatial SEIR model for COVID-19 in South Africa UR - http://hdl.handle.net/10204/12577 ER - en_ZA
dc.identifier.worklist 26364 en_US


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