Time-series forecasting and analysis of COVID-19 outbreak in highly populated countries: A data-driven approach

Arunkumar, P. M.; Ramasamy, L. K.; Amala Jayanthi, M.

A novel corona virus, COVID-19, is spreading across different countries in an alarming proportion, and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling, and forecasting are important research areas that explore the hidden insights from larger set of time-bound data for arriving at better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020. A novel time-series forecasting approach based on auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from the medical and scientific communities to gauge the trend of the disease spread and improvise containment strategies accordingly. © This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License