A Review on Use of Data Science for Visualization and Prediction of the COVID-19 Pandemic and Early Diagnosis of COVID-19 Using Machine Learning Models

Abstract

On December 30, 2019, the WHO China office was informed of a pneumonia-like disease with unknown etiology, from the Wuhan city of China. This disease was found to be caused by a new type of coronavirus. The virus was named severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) and the disease caused by it was named as COVID-19. On March 11, the WHO declared COVID-19 a pandemic. The testing for COVID-19 disease can be broadly classified into two main techniques, firstly, by testing the patient’s blood for immunoassays and second by PCR. The above two techniques are quite costly. Due to this, large-scale testing in developing countries like India is not practically possible. The novel coronavirus is highly infectious, and it spreads from one person to another even before the symptoms have appeared. So, the early detection of the virus will be a great way to stop this global pandemic from causing any more devastation and controlling its spread. In this paper, we review the role of technologies like artificial intelligence and deep learning in early detection, diagnosis, analysis (cure), and socio-economic impact of COVID-19. The purpose of this review paper is to provide a concise but judicious source of information to look over all the possible solutions. Technologies used: Artificial neural networks—Artificial neural networks are based on human brain and nervous system. An artificial neural network consists of several neurons and an activation function. ANNs have been used in diagnosis and early detection of several diseases like dengue and pneumonia. The same can be done in the case of COVID-19 by training the algorithm with suitable datasets. Deep Learning—Deep learning is a subclass of machine learning consisting of algorithms that are based on artificial neural networks. Deep learning is a very efficient way to handle large amount of data. Python libraries like Tensorflow are used in Linux-based systems for executing deep learning algorithms. Visualization of the Pandemic Several dashboards emerged gradually providing a global overview of the pandemic. Some of these are Upcode and NextStrain. Technologies like Python, Excel, R, and Tableau are used here for extracting data and visualizing them in the form of graphs and tables for the general public to understand. Early Detection and Diagnosis Artificial neural networks and deep learning can be used for early detection and diagnosis of the disease. The X-rays and CT images of the patients can be used as datasets. As of now the number of patients of COVID-19 in the world has increased to more than half a million, thus the dataset for training the neural networks and algorithms is quite large. This situation can be capitalized to make highly accurate neural networks using deep learning algorithms. The data can be extracted from press releases on the Internet and government databases by Web scraping. Libraries like Tensorflow can be used in training the models. Tracking and Prediction Artificial intelligence can be used to track and predict the spread of the coronavirus pandemic. We will try to throw some light on the past works in the area of epidemic prediction using AI. In 2015, neural networks were made for prediction of the Zika virus pandemic. These neural networks need to be trained again in accordance with the datasets of COVID-19. For example, Carnegie Mellon University algorithms used for predicting seasonal flu are being retrained with the datasets of COVID-19.