Predictions and visualization for confirmed, recovered and deaths COVID-19 cases in Iraq

The 2019-2020 coronavirus pandemic is an emerging infectious disease that has been referred to as the "COVID-19", which results from the coronavirus "SARS-CoV-2" that started in Wuhan, China, in Dec. 2019 and then spread worldwide. In this paper, an attempt for compiling and analyzing the information of the epidemiological outbreaks on "COVID-19" based upon datasets on "2019-nCoV" has been presented. An empirical data analysis with the visualizations was conducted for understanding the numbers of the variety of the cases that have been reported (i.e confirmed, deaths, and recoveries) in and outside of Iraq and carried out a dynamic map visualization of the " COVID-19" expansion in a global manner through the date wise and in Iraq. We an investigation has been carried out as well, which characterized the pandemic effects Iraq and the entire world, with the use of machine learning. A k-nearest neighbor (kNN) model and a linear regression (LR) model have been proposed. This paper included the precise analysis of the confirmed cases, as well as the recovered cases, deaths, predicting the pandemic viral attacks and how far it is expanding in Iraq and the world, the LR model got the highest results, reaching 100 percent. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Classification of a COVID-19 dataset by using labels created from clustering algorithms

Novel coronavirus (COVID-19) is a newly discovered infectious disease that has received much attention in the literature because of its rapid spread and daily global deaths attributable to such disease. The White House, together with a coalition of leading research groups, has published the freely available COVID-19 Open Research Dataset to help the global research community apply the recent advances in natural language processing and other AI techniques in generating novel insights that can support the ongoing fight against this disease. In this paper, the hierarchical and k-means clustering techniques are used to create a tool for identifying similar articles on COVID-19 and filtering them based on their titles. These articles are classified by applying three data mining techniques, namely, random forest (RF), decision tree (DT) and bagging. By using this tool, specialists can limit the number of articles they need to study and pre-process these articles via data framing, tokenisation, normalisation and term frequency-inverse document frequency. Given its 2D nature, the dimensionality of this dataset is reduced by applying t-SNE. The aforementioned data mining techniques are then cross validated to test the accuracy, precision and recall performance of the proposed tool. Results show that the proposed tool effectively extracts the keywords for each cluster, with RF, DT and bagging achieving optimal accuracies of 98.267%, 97.633% and 97.833%, respectively. © 2021 Institute of Advanced Engineering and Science. All rights reserved.