Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

Scraping bots or scrapers are computer programs that automatically fetch data from the Internet. The price of products is illegally copied from different e-commerce stores and pasted into their sites for their benefit. Various web traffic surveys show that automated programs account for approximately half of all website traffic. However, an E-commerce website concerns many security issues that remain unresolved despite a significant increase in E-commerce development. One of the most damaging attacks on E-commerce sites is the price scraping when it comes to competitors. This paper aims to uncover the fact that both humans and bots are involved in stealing the prices of products from different e-commerce stores without the permission of original sites. This paper discusses the well-known price scraping tools and techniques. First, we have performed real-time price scraping attacks through custom XPATH, and we have obtained results which show that some platforms are still vulnerable for bots e.g., Alibaba and eBay. These sites do not restrict the copy of XPATH. Whereas, Amazon and daraz.pk blocks the price code from HTML document for the scraper to copy the custom XPATH. Moreover, we propose solutions to mitigate price scraping attacks on different e-commerce stores.