Online ISSN: 2515-8260

Keywords : Accumulated at Covid-19

Predictive study of the end of the Covid-19 pandemic in Morocco by regression, and ARIMA modeling (p, d, q)

Majdouline Larif; Adnane Aouidate; Mohammed Bouachrine; Tahar Lakhlifi; Abdelmajid Soulaymani

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 2009-2018

Objective and methods: The objective of our study is to provide forecasts on the key data of the
epidemiological situation in Morocco in order to predict the number of beds in hospitals.The data sources
used in this study are official and they were daily collected updated with information from the Moroccan
Ministry of Health at 6:00 p.m. before the month of Ramadan and 4:00 p.m. for this month.
The autoregressive integrated moving average ARIMA was applied to real-time for the two month
Predictions on the Moroccan population. ARIMA models were able to estimate the number of positive cases
confirmed based on two criteria. The first criterion is to determine the reliability of the statistics and the
second one is to measure the accuracy of forecasting ability of the model equation. The sparse model with
the lowest order of the (AR) or (MA) and (RMSE) values of the forecasts for each dataset was considered the
Result and Conclusion: The ARIMA (1,0,0), ARIMA (9,0,0) and ARIMA (10,0,1) models were deemed to be
the best suited to provide the best possible model to predict the number of positive cases for two months of
prediction of the coronavirus disease 2019 (Covid-19).
However, the ARIMA model (10,0,1) predicts the best model with an expected end of home confinement at
the end of June 2020 with an epidemiological peak of 5000 accumulated cases caused by the coronavirus
disease 2019 (Covid-19) on 13/05/2029.The models were able to predict the number of confirmed cases of the
coronavirus disease 2019 (Covid-19) within a range of two months in Morocco. Thus, it can be a useful tool
for health officials to improve management of the fight against the pandemic and to warn in advance of the
spread of the pandemic.