Evaluating prediction of COVID-19 at provincial level of South Africa: a statistical perspective

• Published in: Environmental science and pollution research international Vol. 29; no. 15; pp. 21289 - 21302
• Main Authors: Arashi, Mohammad, Bekker, Andriette, Salehi, Mahdi, Millard, Sollie, Botha, Tanita, Golpaygani, Mohammad
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022; Springer Nature B.V
• Subjects: Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Coronaviruses; COVID-19; COVID-19 - epidemiology; Disease control; Disease hot spots; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Geography; Humans; Modelling; Pandemics; Research Article; South Africa - epidemiology; Spatial Analysis; Statistical prediction; Viral diseases; Waste Water Technology; Water Management; Water Pollution Control; South Africa; COVID-19; South Africa; Logistic modelling; Moran’s I; Geographical distance; Adjacent distance
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-17291-y

What is the impact of COVID-19 on South Africa? This paper envisages to assist researchers in battling of 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 weight matrix for both absolute and relative counts. Furthermore, generalized logistic growth curve modelling is used for prediction of the COVID-19 spread. We expect this data-driven modelling to provide some insights into hotspot identification and timeous action controlling the spread of the virus.