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16-10-2020 | Issue 1/2021

Annals of Data Science 1/2021

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

Journal:
Annals of Data Science > Issue 1/2021
Authors:
Aman Khakharia, Vruddhi Shah, Sankalp Jain, Jash Shah, Amanshu Tiwari, Prathamesh Daphal, Mahesh Warang, Ninad Mehendale
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Abstract

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.

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