Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning

• Published in: Annals of data science Vol. 8; no. 1; pp. 1 - 19
• Main Authors: Khakharia, Aman, Shah, Vruddhi, Jain, Sankalp, Shah, Jash, Tiwari, Amanshu, Daphal, Prathamesh, Warang, Mahesh, Mehendale, Ninad
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Autoregressive moving average; Business and Management; Coronaviruses; COVID-19; Economics; Finance; Insurance; Machine learning; Management; Outbreaks; Prediction models; Resource management; Statistics for Business; Viral diseases; COVID-19; COVID-19 outbreak prediction; Machine learning
• ISSN: 2198-5804; 2198-5812; 2198-5812
• DOI: 10.1007/s40745-020-00314-9

The Coronavirus Disease-2019 (COVID-19) pandemic persists to have a mortifying impact on the health and well-being of the global population. A continued rise in the number of patients testing positive for COVID-19 has created a lot of stress on governing bodies across the globe and they are finding it difficult to tackle the situation. We have developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries. The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9%  ± 3.9% was developed for 10 high population and high density countries. The highest accuracy of 99.93% was achieved for Ethiopia using Auto-Regressive Moving Average (ARMA) averaged over the next 5 days. The proposed prediction models used by us can help stakeholders to be prepared in advance for any sudden rise in outbreak to ensure optimal management of available resources.