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

Aman Khakharia¹, Vruddhi Shah¹, Sankalp Jain¹

¹K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.

Annals of Data Science

|April 16, 2024

Summary

This summary is machine-generated.

This study developed a COVID-19 outbreak prediction system using machine learning for 10 highly populated countries. The models forecast new cases for 5 days, aiding resource management during the pandemic.

Keywords:

COVID-19 COVID-19 outbreak prediction Machine learning

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Published on: April 9, 2021

Area of Science:

Epidemiology
Public Health
Machine Learning

Background:

The COVID-19 pandemic continues to significantly impact global health and strain public health resources.
Managing the escalating number of COVID-19 cases presents substantial challenges for governing bodies worldwide.

Purpose of the Study:

To develop and evaluate machine learning models for predicting COVID-19 outbreaks.
To forecast the number of new COVID-19 cases over a 5-day period in highly populated countries.

Main Methods:

Utilized 9 distinct machine learning algorithms to build prediction models.
Focused on the 10 most highly and densely populated countries globally.
Validated model performance with an average accuracy of 87.9% ± 3.9%.

Main Results:

Developed a COVID-19 prediction system with an average accuracy of 87.9% ± 3.9% for the selected countries.
Achieved a peak accuracy of 99.93% for Ethiopia using the Auto-Regressive Moving Average (ARMA) model over a 5-day forecast.
The models demonstrated effectiveness in predicting the rise of new COVID-19 cases.

Conclusions:

The developed prediction models can assist stakeholders in proactive preparation for sudden surges in COVID-19 cases.
Effective resource management can be ensured through advance preparation facilitated by these predictive tools.
The study highlights the utility of machine learning in managing public health crises like the COVID-19 pandemic.