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Title |
A Machine Learning Univariate Time series Model for Forecasting COVID-19 Confirmed Cases: A Pilot Study in Botswana |
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Author |
Ofaletse Mphale, Ezekiel U Okike, and Neo Rafifing |
| Citation |
Vol. 22 No. 1 pp. 225-233 |
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Abstract |
The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic. |
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Keywords |
COVID-19, Coronavirus, ARIMA, Box-Jenkin, Time series, Machine learning, ACF, PACF, AIC |
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