On-Ground Distributed COVID-19 Variant Intelligent Data Analytics for a Regional Territory. (10th December 2021)

• Record Type: Journal Article
• Authors: Khuhawar, Umrah Zadi; Siddiqui, Isma Farah; Arain, Qasim Ali; Siddiqui, Mokhi Maan; Qureshi, Nawab Muhammad Faseeh
• Other Names: Khattak Hasan Ali Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-12-10
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/1679835
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : The onset of the COVID-19 pandemic and the subsequent transmission among communities has made the entire human population extremely vulnerable. Due to the virus's contagiousness, the most powerful economies in the world are struggling with the inadequacies of resources. As the number of cases continues to rise and the healthcare industry is overwhelmed with the increasing needs of the infected population, there is a requirement to estimate the potential future number of cases using prediction methods. This paper leverages data-driven estimation methods such as linear regression (LR), random forest (RF), and XGBoost (extreme gradient boosting) algorithm. All three algorithms are trained using the COVID-19 data of Pakistan from 24 February to 31 December 2020, wherein the daily resolution is integrated. Essentially, this paper postulates that, with the help of values of new positive cases, medical swabs, daily death, and daily new positive cases, it is possible to predict the progression of the COVID-19 pandemic and demonstrate future trends. Linear regression tends to oversimplify concepts in supervised learning and neglect practical challenges present in the real world, often cited as its primary disadvantage. In this paper, we use an enhanced random forest algorithm. It is a supervised learning algorithm that is used for classification. This algorithm works well for an extensive range of data items, and also it is very flexible and possesses very high accuracy. For higher accuracy, we have also implemented the XGBoost algorithm on the dataset. XGBoost is a newly introduced machine learning algorithm; this algorithm provides high accuracy of prediction models, and it is observed that it performs well in short-term prediction. This paper discusses various factors such as total COVID-19 cases, new cases per day, total COVID-19 related deaths, new deaths due to the COVID-19, the total number of recoveries, number of daily recoveries, and swabs through the proposed technique. This paper presents an innovative approach that assists health officials in Pakistan with their decision-making processes.