Simulation of the Final Size of the Evolution Curve of Coronavirus Epidemic in Morocco using the SIR Model

Abstract

Since the epidemic of COVID-19 was declared in Wuhan, Hubei Province of China, and other parts of the world, several studies have been carried out over several regions to observe the development of the epidemic, to predict its duration, and to estimate its final size, using complex models such as the SEIR model or the simpler ones such as the SIR model. These studies showed that the SIR model is much more efficient than the SEIR model; therefore, we are applying this model in the Kingdom of Morocco since the appearance of the first case on 2 March 2020, with the objective of predicting the final size of the epidemic.

Simulation of the evolution of the Covid-19 pandemic in the United Arab Emirates using the sir epidemical model

“…The model showed that the number of infections decreases in June 2020, reaching an ending phase on 24 June 2020. In another case study (Ifguis, Ghozlani, Ammou, Moutcine, & Abdellah, 2020) This group is for people who have recovered from the disease and are likely to be immune from the disease.…”

Optimal control strategy of COVID-19 spread in Morocco using SEIRD model

“…In Morocco, the optimal estimates of R 0 of COVID-19 ranges between 1.22 and 2.55 [1]. In the context of this paper, the transmissibility if COVID-19 and median are calculated around R 0 = 1.37.…”

An application of the ensemble Kalman filter in epidemiological modelling

“…Mathematical compartmental models, such as SIR (Susceptible—Infectious—Recovered) [ 18 , 19 ], in epidemiology, are generally expressed by a system of ordinary differential equations (ODE). Recent studies on COVID-19 modelling includes using the basic SIR model [ 12 , 18 , 19 ] or its extension (modified) versions such as SEIR (Susceptible—Exposed—Infectious—Recovered) [ 7 , 10 , 11 , 19 – 21 ], SIRD (Susceptible—Infectious—Recovered—Dead) [ 1 – 4 , 16 , 17 , 22 ] and SEIRD (Susceptible—Exposed—Infected—Recovered—Dead) [ 13 – 15 ].…”

“…Model parameters can be estimated by solving inverse problems using observational data [ 24 ]. Some recent works on inverse COVID-19 modelling with different models include works of Wan et al [ 7 ], Sun et al [ 10 ], Libotte et al [ 18 ], Lobato et al [ 2 ], Li et al [ 11 ], Anastassopoulou et al [ 3 ], Loli Piccolomini and Zama [ 14 ], Korolev [ 15 ], Ndaïrou et al [ 9 ], Ifguis et al [ 12 ], Engbert et al [ 21 ], Yang et al [ 20 ], Arroyo-Marioli et al [ 25 ], and Ghostine et al [ 26 ]. Recent methods of inverse modelling for parameter estimation include the least-square techniques and optimization algorithms [ 3 , 10 , 12 , 14 , 15 ], Differential Evolution method [ 18 ], Stochastic and Multiobjective Fractal Search algorithm [ 2 ], and data assimilation methods [ 11 , 20 , 21 , 25 , 26 ].…”