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Barrie, M.B., Lakoh, S., Kelly, J.D., Kanu, J.S., Squire, J., Koroma, Z., Bah, S., Sankoh, O., Brima, A., Ansumana, R., Goldberg, S.A., Chitre, S., Osuagwu, C., Maeda, J., Barekye, B., Numbere, T.W., Abdulaziz, M., Mounts, A., Blanton, C., Singh, T., Samai, M., Vandi, M.A. and Richardson, E.T. (2021) SARS-CoV-2 Antibody Prevalence in Sierra Leone, March 2021: A Cross-Sectional, Nationally Representative, Age-Stratified Serosurvey. BMJ Glob Health, 6, e007271.
https://doi.org/10.1136/bmjgh-2021-007271

has been cited by the following article:

• TITLE: Modeling and Forecasting of SARS CoV-2 Cases in Sierra Leone

  AUTHORS: Sallieu Kabay Samura, Theresa Ruba Koroma, Abdul A. Kamara

  KEYWORDS: ARIMA Model, SARS Cov-2, Stationarity, Forecast

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.10 No.4, April 8, 2022

  ABSTRACT: Severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) has been a global threat spreading in Sierra Leone, and many studies are being conducted using various Statistical models to predict the probable evolution of this pandemic. In this paper, we use the autoregressive integrated moving average (ARIMA) model with the aim of forecasting the cumulative confirmed cases of SARS CoV-2 in Sierra Leone. The Akaike Information Criterion (AIC) was applied to the training data as a criterion method to select the best model. In addition, the statistical measure RMSE and MAPE were utilized for testing this data, and the model with the minimum RMSE and MAPE was selected for future forecasting. ARIMA (3, 2, 1) was confirmed to be the optimal model based on the lowest AIC value. This model was then applied to study the trend of SARS CoV-2 from 1st February 2022 to 30th February 2022. The result shows that incidence of SARS CoV-2 from 1st February 2022 to 30th February 2022, increasing growth steep in Sierra Leone (7718.629, 95% confidence limit of 6785.985 - 8651.274).