An integrated GIS-based multi-criteria decision analysis (MCDA) approach to model COVID-19 vulnerability in Gauteng South Africa

Jele, Jabulani
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Gauteng province is the epicentre of COVID-19 in South Africa and to date, the province accounts for 32.5% of the country’s total COVID-19 cases. The severity of COVID-19 in Gauteng requires specialised methodologies that can help identify areas vulnerable to the disease and for support mechanisms to be directed within them. By integrating two (2) GIS Based MCDA criteria indicator weight determining algorithms, namely; the subjective analytical hierarchy process (AHP) and the objective Entropy Weight Method (EWM), an integrated ward level COVID-19 vulnerability model for the province was developed. The receiver operating characteristic and area under the curve (ROC-AUC) method was used to validate the accuracy of the models. And with an AUC score of 0.703, the integrated COVID-19 vulnerability model was the most accurate for modelling COVID-19 vulnerability in Gauteng wards, as opposed to the individual AHP and EWM models with AUC scores of 0.634 and 0.686 respectively. The study demonstrates that the integrated COVID-19 vulnerability model is the most ideal model to help guide government intervention efforts in the fight against the disease in Gauteng.
A dissertation submitted in fulfilment of the requirements for the degree of Master of Science in Geographic Information Systems and Remote Sensing to the Faculty of Science, School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, 2022