Abstract
In Wuhan China, the world’s most dangerous virus is discovered, which is
named COVID-19 by World Health Organization. Social distancing is one of the
powerful methods to control this virus as it is realized that lockdown is not a permanent
solution. This research chapter aims to identify the major activities influencing the
transmission of the coronavirus spread using Artificial Intelligence bound models. To
conduct this research in the right direction, movement control restriction,
meteorological parameters, and air pollution levels information are collected from
various valid websites. End-to-end data pre-processing steps are carried out in detail to
handle the outliers and missing values and investigate the correlation between
dependent and independent variables. Multiple linear regression, neural networks,
decision trees, and random forests are chosen to fulfil the objective of this research by
identifying the most influential activities and other parameters. Here, the model’s
performance evaluation is done using the R2
value, mean absolute error and mean
squared error. The predicted values are plotted against the actual value to illustrate the
error patterns. Among all models, random forest and decision tree models are proven to
give the highest accuracy of 93 percent and 91 percent respectively. Prescriptive
analysis has been further analyzed by performing feature importance extraction from
the highly accurate models to identify the most impactful parameters the government
authority and healthcare front-liners focus on to mitigate the number of COVID-19
cases daily.