A Survey on an Analysis of Big Data Open Source Datasets, Techniques and Tools for the Prediction of Coronavirus Disease

Abstract

Coronavirus disease-19 (COVID-19), an infectious disease that spreads when people live in close proximity has greatly impacted healthcare systems worldwide. The pandemic has so disrupted human life economically and socially that the scientific community has been impelled to devise a solution that assists in the diagnosis, prevention and outbreak prediction of COVID-19. This has generated an enormous quantum of unstructured data that cannot be processed by traditional methods. To alleviate COVID-19 threat and to process these unstructured data, big data analytics can be used. The main objective of this paper is to present a multidimensional survey on open source datasets, techniques and tools in big data to fight COVID-19. To this end, state-of-the-art articles have been analyzed, qualitatively and quantitatively, to put together a body of work in the prediction of COVID-19. The findings of this review show that machine learning classification algorithms in big data analytics helps design a predictive model for COVID-19 using the open source datasets. This survey may serve as a starting point to enhance the research in COVID-19.