Evaluation and Prediction of Covid-19 Disease Spread across Sixteen West Africa Countries Using a Machine Learning Approach

Michael Segun Olajide

Department of Computer Science, Adeyemi College of Education, Ondo, Nigeria

Corresponding Author: 1olajidems@aceondo.edu.ng

Adekola Alex Ajayi

Department of Computer Science, Adeyemi College of Education, Ondo, Nigeria

Corresponding Author: ajayiaa@aceondo.edu.ng

Oladoyin Anthony Abiodun

Department of Computer Science, Adeyemi College of Education, Ondo, Nigeria

Corresponding Author: o.a.abiodun@lancaster.ac.uk

Oluwagboyega Peter Afolabi

Department of Computer Science, Adeyemi College of Education, Ondo, Nigeria

Corresponding Author:afolabiop@aceondo.edu.ng

Abstract :

The covid-19 pandemic disease created a great scare and massive economic, social, political, and international challenges. Many lives were lost, and economic crisis coupled with crippling industrial activities. This work aims to assess the tragedy, take into account the depth of its spread, and make intelligent guesses and predictions that will help all parties take appropriate steps to stop the disease. Using the Covid-19 time series dataset, an analysis was conducted to reveal the confirmed, active, recovery, and death cases of Covid-19 cases in the 16 West African countries. Using three machine learning algorithms: Polynomial Regression (PR), Support Vector Machine (SVM), and Neural Network (NN) to carry out prediction, the polynomial regression model gave the best result when considering the generated results among the three techniques employed for the study.


[1] Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Timon R., & Atkinson, P. M. 2020. Covid-19 outbreak prediction with ML. Algorithms, 13(10), 249-261.

[2] Debjit, K., Islam, M. S., Rahman, M., Pinki, F. T., Nath, R. D., Al-Ahmadi, S., ... & Awal, M. 2022. An improved machine-learning approach for COVID-19 prediction using Harris Hawks optimization and feature analysis using SHAP. Diagnostics, 12(5), 1023-1042.

[3] Hamzah, F. B., Lau, C., Nazri, H., Ligot, D. V., Lee, G., Tan, C. L., Shaib, M. K. B. M., Zaidon U. H. B., Abdullah A. B. & Chung, M. H. 2020. CoronaTracker: worldwide COVID-19 outbreak data analysis and prediction. Bull World Health Organ, 1(32), 1-32.

[4] Humanitarian Data Exchange 2022. Novel Coronavirus (COVID-19) Cases Data. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases. Accessed 10 April 2022

[5] Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. 2020. Applications of ML and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, 110059-110086.

[6] Moulaei, K., Shanbehzadeh, M., Mohammadi-Taghiabad, Z., & Kazemi-Arpanahi, H. 2022. Comparing ML algorithms for predicting COVID-19 mortality. BMC medical informatics and decision making, 22(1), 1-12.

[7] Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B. W., Aslam, W., & Choi, G. S. 2020. COVID 19 future forecasting using supervised ML models. IEEE Access, 8, 101489-101499.

[8] Ontario Public Health 2021. Evidence Brief: COVID-19 Variant of Concern Omicron (B.1.1.529): Risk Assessment, December 7, 2021. https://www.publichealthontario.ca/-/media/documents/ncov/voc/2021/12/covid-19-omicron-b11529-risk-assessment-dec-7.pdf?sc_lang=en. Accessed 4 January 2022

[9] Sujath, R., Chatterjee, J. M., & Hassanien, A. E. 2020. A ML forecasting model for COVID pandemic in India. Stochastic Environmental Research and Risk Assessment, 34(7), 959-972.

[10] Tucker, H. 2020. Coronavirus bankruptcy tracker: These major companies are failing amid the shutdown. https://www.forbes.com/sites/hanktucker/2020/05/03/coronavirus-bankruptcy-tracker-these-major-companies-are-failing-amid-theshutdown/#5649f95d342 Accessed 11 March 2022

[11] Wikipedia. 2022. Covid-19 Pandemic. https://en.wikipedia.org/wiki/COVID-19_pandemic Accessed 10 April 2022

[12] World Health Organization. 2020. Impact of COVID-19 on people's livelihoods, their health and our food systems. https://www.who.int/news/item/13-10-2020 Accessed 17 September 2021

[13] World Health Organization. 2022. Enhancing response to Omicron SARS-CoV-2 variant: Technical brief and priority actions for the Member States. World Health Organization Headquarters, Geneva, Switzerland. Update, (6).

[14] Worldometers. 2020. West Africa Population. https://www.worldometers.info/world-population/western African-population/ Accessed 12 September 2021

[16] Xiong, Y., Ma, Y., Ruan, L., Li, D., Lu, C., & Huang, L. (2022). Comparing different ML techniques for predicting COVID-19 severity. Infectious diseases of poverty, 11(1), 1-9.

[16] Zhao, Z., Li, X., Liu, F., Zhu, G., Ma, C., & Wang, L. 2020. Prediction of the COVID-19 spread in African countries and implications for prevention and control: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal, and Kenya. Science of the Total Environment, 729, 138959.

[17] Zheng, N., Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., Yang, T., Lou, B., Chi, Y., Long, H., Ma, M., Yuan, Qi., Zhang, S., Zang, D., Ye, F. & Xin, J. 2020. Predicting COVID-19 in China using a hybrid AI model. IEEE transactions on cybernetics, 50(7), 2891-2904.

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