COVID-19 DATAANALYSIS OF INDIA USING PYTHON

Abstract

The outbreak of COVID-19 in different parts of the world is a major concern for all the administrative units of respective countries. India is also facing this very tough task for controlling the virus outbreak and has managed its growth rate through some strict measures. This analysis presents the current situation of coronavirus spread in India along with the impact of various measures taken for it. With the help of data sources (till 10th of June ) from various state units of India and Ministry of Health and Family Welfare, Government of India, this study presents various trends and patterns. This study answers six different research questions in a comprehensive manner. It has been reported that the growth rate of infected cases has been controlled with the help of National Lockdown, however some uncontrolled mass level events had negatively impacted the infected cases. It appears that only essential services should be open for the citizens of India and the na- tional lockdown should be carried on for next 2-4 months. This study will be useful for the Government of India and various states of India, Administrative Units of India, Frontline health workforce of India, researchers and scientists. This study will also be favorable for the administrative units of other countries to consider various aspects related to the control of COVID-19 is widespread in their respective regions. COVID-2019 has been recognized as a global threat, and several studies are being conducted in order to contribute to the fight and prevention of this pandemic. This work presents a scholarly production dataset focused on COVID-19, providing an overview of scientific research activities, making it possible to identify countries, scientists and research groups most active in this task force to combat the coronavirus disease. The dataset is composed of a number of records of articles’ metadata collected from Scopus, PubMed, arXiv and bioRxiv databases from January 2019 to July 2020. Those data were extracted by using the techniques of Python Web Scraping and preprocessed with Pandas Data Wrangling. In addition, the pipeline to preprocess and generate the dataset are versioned with the Data Version Control tool (DVC) and are thus easily reproducible and auditable