Spatial analysis and prediction of COVID-19 spread in South Africa after lockdown

TL;DR

This study analyzes the spatial distribution and predicts the spread of COVID-19 in South Africa post-lockdown using heatmaps, Moran index, and logistic growth models to aid decision-making.

Contribution

It introduces a combined spatial analysis and predictive modeling approach specifically tailored for COVID-19 spread in South Africa.

Findings

Identification of COVID-19 hotspots using heatmaps

Quantification of spatial autocorrelation with Moran index

Forecasting of disease spread using logistic growth models

Abstract

What is the impact of COVID-19 on South Africa? This paper envisages assisting researchers and decision-makers in battling the COVID-19 pandemic focusing on South Africa. This paper focuses on the spread of the disease by applying heatmap retrieval of hotspot areas and spatial analysis is carried out using the Moran index. For capturing spatial autocorrelation between the provinces of South Africa, the adjacent, as well as the geographical distance measures, are used as a weight matrix for both absolute and relative counts. Furthermore, generalized logistic growth curve modeling is used for the prediction of the COVID-19 spread. We expect this data-driven modeling to provide some insights into hotspot identification and timeous action controlling the spread of the virus.