Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

Version 1 : Received: 6 June 2021 / Approved: 8 June 2021 / Online: 8 June 2021 (10:56:22 CEST)

How to cite: Potgieter, A.; Fabris-Rotelli, I.; Kimmie, Z.; Dudeni-Tlhone, N.; Holloway, J.; Janse Van Rensburg, C.; Thiede, R.; Debba, P.; Docrat, R.; Abdelatif, N.; Makhanya, S. Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa. Preprints 2021, 2021060211 (doi: 10.20944/preprints202106.0211.v1). Potgieter, A.; Fabris-Rotelli, I.; Kimmie, Z.; Dudeni-Tlhone, N.; Holloway, J.; Janse Van Rensburg, C.; Thiede, R.; Debba, P.; Docrat, R.; Abdelatif, N.; Makhanya, S. Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa. Preprints 2021, 2021060211 (doi: 10.20944/preprints202106.0211.v1).

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.

Subject Areas

COVID-19; spatial; mobility; spatial weight matrices; principal component analysis; hierarchical clustering

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