id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-294468-0v4grqa7	Kasilingam, Dharun	Exploring the Growth of COVID‐19 Cases using Exponential Modelling Across 42 Countries and Predicting Signs of Early Containment using Machine Learning	2020-08-04		.txt	text/plain	7484	493	54	This research uses exponential growth modelling studies to understand the spreading patterns of the COVID‐19 virus and identifies countries that have shown early signs of containment until 26(th) March 2020. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% to 92.9% to predict early signs of infection containment. The objective of the research is to develop a mathematical model using exponential growth analysis coupled with machine learning, to predict worldwide COVID-19 early containment signs. Secondly, the research aims at building supervised machine learning models with high accuracies for predicting signs of early containment with infrastructure availability, environmental factors, infection severity factors, and government policies of countries as independent variables. The research presents machine learning models based on variables such as infrastructure, environment, policies, and the infection itself, to predict early signs of containment in the country.	./cache/cord-294468-0v4grqa7.txt	./txt/cord-294468-0v4grqa7.txt