CoviCare: Tracking Covid-19 using PowerBI

• Main Authors: Chakate, Kunal, Giri, Govinda, Gonge, Sudhanshu S., Deshpande, Aditya, Pawade, Yogeshwar, Joshi, Rahul
• Format: Conference Proceeding
• Language: English
• Subjects: Common Symptoms; COVID-19; CovidTracker; Data visualization; Hypothesis Testing; ML/DL models; Normalization; Pandemics; Signal processing; Soft sensors; Standardization
• ISSN: 2643-444X
• DOI: 10.1109/ICSC56524.2022.10009069

In late 2019, the world witnessed the emergence of one of the most alarming pandemics recorded in the Contemporary Age, Covid-19. The new coronavirus is responsible for causing the disease Covid-19 and it has since spread to over 180 countries. As the disease spreads over the world, it has become a global pandemic, threatening global public health, and presenting a huge threat to global civilization. To oppose and prevent the spread of COVID-19, everyone should be well informed on the disease's constantly changing status. To accomplish this purpose, a COVID-19 analytical tracker was developed using PowerBI to provide the most up-to-date sickness status as well as critical analytical insights. The Covid tracker is intended for the general public who lack specialized statistical knowledge. It tries to express insights using a variety of basic and succinct data visualizations backed up by reputable data sources. The purpose of this paper is to describe the key strategies used to create the insights presented on the tracker, which include data retrieval, normalization techniques, and ML/DL models. In addition to explaining the facts and justifications for the approaches used, the paper includes several major discoveries made in relation to COVID-19 utilizing the methodologies.