Analysis and forecasting the outbreak of Covid-19 in Ethiopia using machine learning

Abstract

Coronavirus outbreaks affect human beings as a whole and can be a cause of serious illness and death. Machine learning (ML) models are the most significant function in disease prediction, such as the Covid-19 pandemic, in high-performance forecasting and used to help decision-makers understand future situations. ML algorithms have been used for a long time in many application areas that include recognition and prioritization for certain treatments. Too many ML furcating models are used to deal with problems. In this study, predict a pandemic outbreak using the ML forecasting models. The models are designed to predict Covid-19, depending on the number of confirmed cases, recovered cases and death cases, based on the available dataset. Support Vector Machine (SVM) and Polynomial Regression (PR) models were used for this study to predict Covid-19 ‘s aggressive risk. All three cases, such as confirmed, recovered and death, models predict death in Ethiopia over the next 30 days. The experimental result showed that SVM is doing better than PR to predict the Covid-19 pandemic. According to this report, the pandemic in Ethiopia increased by half between the mid of July 2020. Then Ethiopia will face a number of hospital shortages, and quarantine place.

Keywords: COVID-19, Forecasting, coronavirus, machine learning, polynomial regressing, support vector machine

Article Review Status: Published

Pages: 1-13 (Download PDF)

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