AI-Enabled Covid-19 Prediction Methods and Anti-Covid Strategies

ABSTRACT

SARS-CoV-2, also known as the Coronavirus, is a disease belonging to the SARS-CoV family, first reported in December 2019 in Wuhan, China. It has since spread to several countries and has become a global threat. The issues of the Covid-19 pandemic are being tackled with the help of technological breakthroughs. Artificial Intelligence (AI) and Machine Learning (ML) play crucial roles in addressing this problem. Many organizations have been developing various devices to monitor the health parameters of a person from time to time. This work aims to study these parameter values and identify patterns within the data to predict whether a person is infected with Covid-19 or not. Different Classification algorithms such as Decision Tree, Random Forest, Support Vector Machine, Naive Bayes, and Logistic Regression are employed. The Random Forest showed the highest performance among the algorithms mentioned above, demonstrating 99.03% accuracy. This article also suggests Anti-Covid Strategies, such as Mask Detection and Social Distancing. A Mask Detection model is constructed utilizing Transfer Learning and existing Image Classification Networks, and the best one is VGG-19 which has obtained 99.31% test accuracy. © 2022 IEEE.