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

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 sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

Changes in trauma-related emergency medical services during the COVID-19 lockdown in the Western Cape, South Africa

“…Unfortunately, the dataset used in the study does not contain information about potential exposure to injury. Data modelling of mobility in South Africa during the COVID-10 pandemic, suggests reductions in mobility of up to 78% during Lockdown Level 5 [ 39 ], however the data does not discriminate time of mobility. To our knowledge no other studies have investigated time of injury during the COVID-19 related lockdowns.…”

A Spatial SEIR Model for COVID-19 in South Africa

“…Using different movement matrices at the different lockdown interventions also allowed this mixing to vary at different stages of the disease spread. Alternative mobility data could also be considered, see for example, Potgieter et al (2021).…”

The effect of changing COVID-19 restrictions on the transmission rate in a veterinary clinic

“…Many mathematical models have included key features of the spread of the COVID-19 disease, such as distinguishing between asymptomatic infected individuals from the pre-symptomatic and symptomatic infected individuals ( Chang et al, 2021 ; Keskinocak et al, 2020 ; Parajuli et al, 2020 ), while some early papers did not include asymptomatic compartments ( Mukandavire et al, 2020 ; Nyabadza et al, 2020 ). Many different modeling techniques have been used, ranging from agent-based approaches to Bayesian modeling frameworks at the city level or county level ( Murtadha et al ; Olmo & Sanso-Navarro, 2021 ; Potgieter et al, 2021 ). Some ODE models have included compartments with individuals in the hospital, with some assuming those persons to be isolated or others assuming them to be transmitting disease but at a lower rate ( Edholm et al, 2022 ; Gumel et al, 2021 ; Ngonghala et al, 2020 ).…”