Designing a Machine Learning Based Prediction Model for Covid-19 in Ethiopia

Authors

  • Ashenafi Tulu College of Computing and Informatics, P.O. Box 138, Haramaya University, Dire Dawa, Ethiopia
  • Jemal Abate College of Computing and Informatics, P.O. Box 138, Haramaya University, Dire Dawa, Ethiopia
  • Vijayshri Khedkar Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
  • Fikirte Zemene Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India

Keywords:

Machine Learning, time series approach, Prediction, COVID-19, Ethiopia, Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA) model, Seasonal Autoregressive Integrated Moving Average (SARIMA).

Abstract

A global health crisis resulted due to COVID 19, and the medical industry is seeking support of novel technologies to monitor and control disease spread. This study aims to design machine learning-based prediction model for COVID-19 outbreaks in Ethiopia. Large-scale data analytics were used to collect information from the Ethiopian Ministry of Health and WHO reports between March 13, 2020 up to May 16, 2022. The dataset included variables like the number of new cases, tolls and recoveries data from Ethiopia's ten regional states and two administrative cities. Various splitting percentage of the dataset is used as a training set to train the model and a comparison was made for the prediction results of the models. The researchers utilized the AR, ARIMA, and SARIMA models to predict the data accurately, and the best Spearman correlation was used to determine which model was better suited for prediction. It was observed that the ARIMA model performed best at identifying cases that are dead, confirmed, and recovered. For cases that are dead, confirmed, and recovered, respectively, the Spearman correlation ratings for ARIMA were 1.00, 1.00, and 0.93. With less datasets, the AR model did well as well, but the SARIMA model excelled with larger datasets. The proposed machine learning-based prediction model is a useful tool for controlling and monitoring the spread of COVID-19 in Ethiopia.

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Published

30.11.2023

How to Cite

Tulu, A. ., Abate, J. ., Khedkar, V. ., & Zemene, F. . (2023). Designing a Machine Learning Based Prediction Model for Covid-19 in Ethiopia. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 700–706. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4009

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

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