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Modelling representative population mobility for COVID-19 spatial transmission in South Africa

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dc.contributor.author Potgieter, A
dc.contributor.author Fabris-Rotelli, IN
dc.contributor.author Kimmie, N
dc.contributor.author Dudeni-Tlhone, Nontembeko
dc.contributor.author Holloway, Jennifer P
dc.contributor.author Janse van Rensburg, C
dc.contributor.author Thiede, R
dc.contributor.author Debba, Pravesh
dc.contributor.author Manjoo-Docrat, R
dc.contributor.author Abdelatif, N
dc.contributor.author Makhanya, S
dc.date.accessioned 2021-11-17T07:15:48Z
dc.date.available 2021-11-17T07:15:48Z
dc.date.issued 2021-10
dc.identifier.citation Potgieter, A., Fabris-Rotelli, I., Kimmie, N., Dudeni-Tlhone, N., Holloway, J.P., Janse van Rensburg, C., Thiede, R. & Debba, D. et al. 2021. Modelling representative population mobility for COVID-19 spatial transmission in South Africa. <i>Frontiers in Big Data, 4.</i> http://hdl.handle.net/10204/12150 en_ZA
dc.identifier.issn 2624-909X
dc.identifier.uri https://doi.org/10.3389/fdata.2021.718351
dc.identifier.uri http://hdl.handle.net/10204/12150
dc.description.abstract The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://www.frontiersin.org/articles/10.3389/fdata.2021.718351/full en_US
dc.source Frontiers in Big Data, 4 en_US
dc.subject Covid-19 en_US
dc.subject Reduced mobility en_US
dc.subject Spatial weight matrices en_US
dc.subject Principal component analysis en_US
dc.subject Hierarchical clustering en_US
dc.title Modelling representative population mobility for COVID-19 spatial transmission in South Africa en_US
dc.type Article en_US
dc.description.pages 22 en_US
dc.description.note Copyright © 2021 Potgieter, Fabris-Rotelli, Kimmie, Dudeni-Tlhone, Holloway, Janse van Rensburg, Thiede, Debba, Manjoo-Docrat, Abdelatif and Khuluse-Makhanya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. 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 Potgieter, A., Fabris-Rotelli, I., Kimmie, N., Dudeni-Tlhone, N., Holloway, J. P., Janse van Rensburg, C., ... Makhanya, S. (2021). Modelling representative population mobility for COVID-19 spatial transmission in South Africa. <i>Frontiers in Big Data, 4</i>, http://hdl.handle.net/10204/12150 en_ZA
dc.identifier.chicagocitation Potgieter, A, IN Fabris-Rotelli, N Kimmie, Nontembeko Dudeni-Tlhone, Jennifer P Holloway, C Janse van Rensburg, R Thiede, et al "Modelling representative population mobility for COVID-19 spatial transmission in South Africa." <i>Frontiers in Big Data, 4</i> (2021) http://hdl.handle.net/10204/12150 en_ZA
dc.identifier.vancouvercitation Potgieter A, Fabris-Rotelli I, Kimmie N, Dudeni-Tlhone N, Holloway JP, Janse van Rensburg C, et al. Modelling representative population mobility for COVID-19 spatial transmission in South Africa. Frontiers in Big Data, 4. 2021; http://hdl.handle.net/10204/12150. en_ZA
dc.identifier.ris TY - Article AU - Potgieter, A AU - Fabris-Rotelli, IN AU - Kimmie, N AU - Dudeni-Tlhone, Nontembeko AU - Holloway, Jennifer P AU - Janse van Rensburg, C AU - Thiede, R AU - Debba, Debba AU - Manjoo-Docrat, R AU - Abdelatif, N AU - Makhanya, S AB - The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful. DA - 2021-10 DB - ResearchSpace DP - CSIR J1 - Frontiers in Big Data, 4 KW - Covid-19 KW - Reduced mobility KW - Spatial weight matrices KW - Principal component analysis KW - Hierarchical clustering LK - https://researchspace.csir.co.za PY - 2021 SM - 2624-909X T1 - Modelling representative population mobility for COVID-19 spatial transmission in South Africa TI - Modelling representative population mobility for COVID-19 spatial transmission in South Africa UR - http://hdl.handle.net/10204/12150 ER - en_ZA
dc.identifier.worklist 25067 en_US


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