J. Nig. Soc. Phys. Sci. 4 (2022) 843

Journal of the
Nigerian Society

of Physical
Sciences

Forecasting of the epidemiological situation: Case of COVID-19
in Morocco

El Mehdi Chouita,∗, Mohamed Rachdib, Mostafa Bellafkiha, Brahim Raouyanec

aRAISS Laboratory, Department of Mathematics and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco
bSchool of Engineering - School of Management (ISGA), Casablanca, Morocco.

cFaculty of Sciences Ain Chock, university Hassan II (FSAC), Casablanca, Morocco.

Abstract

Since the coronavirus pandemic started, many people have died due to the disease. The epidemic has been challenging to predict, as it progresses
and spreads throughout the world. We used Auto-Regressive Integrated Moving Average (ARIMA) models to predict the outbreak of COVID-19
in the upcoming months in Morocco. In this work, we measured the effective reproduction number using the real data and the forecasted data
produced by the two commonly used approaches, to reveal how effective the measures taken by the Moroccan government have been in controlling
the COVID-19 outbreak. The prediction results for the next few months show a strong evolution in the number of confirmed and death cases in
Morocco. We study the spread of COVID-19 in Morocco to see how many cases are discovered, recovered, and dead, and the forecasting of
further cases is used as a basic novel method. It is based on time series models. We used coronavirus outbreak data from March 02, 2020, to
August 04, 2021. ARIMA (Autoregressive integrated moving average) and Prophet time-series models are used to forecast the development of
COVID-19, which is not a novel method. The mean absolute error, root mean square error, and coefficient of determination R2 were computed
to assess the model’s performance. Our study aims to provide a better understanding of the infectious disease outbreak that affected Morocco. It
also provides information on the disease outbreak’s epidemiology. Our study shows that the FBProphet model is more accurate in predicting the
prevalence of COVID-19. It can help guide the government’s efforts to prevent the virus’ spread.

DOI:10.46481/jnsps.2022.843

Keywords: ARIMA, COVID-19, Pandemic, FBProphet, Time series forecasting

Article History :
Received: 30 May 2022
Received in revised form: 11 September 2022
Accepted for publication: 11 September 2022
Published: 11 October 2022

c© 2022 The Author(s). Published by the Nigerian Society of Physical Sciences under the terms of the Creative Commons Attribution 4.0 International license

(https://creativecommons.org/licenses/by/4.0). Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Communicated by: B. J. Falaye

1. Introduction

COVID-19 is a new respiratory virus that emerged in China
in 2019. It has a high mutation and is easily spreadable. Peo-
ple with compromised immune systems or chronic illnesses
such as diabetes or heart disease can develop severe respira-
tory conditions if they become infected with this virus [1]. In

∗Corresponding author tel. no: +212 662 331 895
Email address: elmehdi.chouit@gmail.com (El Mehdi Chouit )

this paper, we are going to forecast the further COVID cases
in Morocco. So, before time, we can save the lives of people.
The outbreak of COVID-19 was designated a pandemic by the
World Health Organization (WHO) on March 11, 2020. Look-
ing into these numbers of confirmed and death cases, we know
the extreme danger of this pandemic situation. In Morocco,
the COVID-19 pandemic was confirmed start to propagate on
2 March 2020, when the first confirmed COVID-19 case was
found in Casablanca. A few weeks later, many activities and

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Chouit et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 843 2

shops were closed by the Moroccan authorities to contain the
spread of this new virus. Infections can be difficult to contain
since individuals may not exhibit symptoms for a long time.
This can be achieved through social isolation, and the only way
to stop the spread of COVID-19 is by quarantining or isolation
of infected individuals. COVID-19’s spread can be divided into
three stages [2]:

1. Local outbreak: At this stage, the virus’s spread can be
tracked, and it can be identified as the source of infection.
Most cases are mild or moderate. The ongoing Coro-
navirus (COVID-19) pandemic has underscored the im-
portance of working together, in partnership, to respond
to outbreaks of infectious disease. It has highlighted the
importance of having a clear plan and of everyone be-
ing aware of each others roles and responsibilities in the
event of a pandemic

2. Community transmission: The source of the transmission
chain can’t be determined at this time. Symptoms of the
illness can be found in different parts of the country. The
risk of breathing these in is highest when people are near,
but they can be inhaled over longer distances, particu-
larly indoors. Transmission can also occur if splashed
or sprayed with contaminated fluids in the eyes, nose or
mouth, and, rarely, via contaminated surfaces.

3. Large-scale transmission:Due to large-scale uncontrolled
human mobility, the virus is spreading rapidly in vari-
ous parts of the country at this stage. Close-contact set-
tings, especially where people have conversations very
near each other. Confined and enclosed spaces with poor
ventilation.

Because of the spread of the coronavirus, which has a global
reach, national governments have imposed a lockdown to pre-
vent its infection. As of Dec. 24, 2021, 278,588,451 cases
had been confirmed, 249,356,570 cases had been recovered,
5,402,557 fatalities had been recorded, and 23,829,324 active
cases had been detected throughout the globe [3] was used to
compile the statistical data.

1.1. Key Motivation
The key motivation behind the current research work is: to

accurately forecast the spread of COVID-19 in Morocco which
could help the Govt officials better plan to minimize its impact.
The goal of this research is to model and predict the effects
of Coronavirus transmission on various public services and re-
sources. Various studies [4-8] have been published on the use
of statistical analysis and artificial intelligence to predict the
spread of the Covid19 virus and its effects. The findings are
based on the limited data that was available during the outbreak.
COVID-19 predictions are used to help predict the spread of in-
fectious diseases. They are also used to prepare for and limit
the spread of diseases [9].

In this paper, we study the capacities of the ARIMA [10]
and FBProphet [11] models for predicting future outcomes.
Many prediction models are used to forecast the impact of
COVID-19 on Morocco. This paper aims to analyze these mod-
els and find out their applicability to predicting the virus impact

in the country. COVID-19 trend analysis and model perfor-
mance are analyzed using various metrics. These include mean
absolute error (MAE), root mean square error (RMSE) and co-
efficient of determination (R2). We produce predicting results
for COVID-19 confirmed, recovered, and death cases.

1.2. General context

Because of the geographic position of Morocco and its
proximity is near to Europe, where the virus is still exten-
sively spreading, the infection spread has been discovered. On
March 2, 2020, Morocco became aware of the first Coronavirus
case, with 954 199 persons infected with COVID-19, result-
ing in 937,122 people being cured and 14,823 fatalities, while
8,801,829 cases were ruled out due to negative laboratory test-
ing (According to the Official Portal of the Coronavirus in Mo-
rocco www.covidmaroc.ma on 24/12/2021). To control and
limit the spread of the pandemic. Morocco has taken the fol-
lowing measures and measures:

1. All air and marine flights to or from countries with epi-
demic hotspots will be suspended on March 13, 2020.

2. On March 15, 2020, the King ordered the government to
quickly create a special fund to deal with the epidemic

3. Morocco declares a health emergency on March 19,
2020;

4. On March 12, 2020, the COVID-19 is given access to
field hospitals and private clinics.

5. On April 7, 2020, the mask is made mandatory for the
whole country.

Given the economic and societal consequences of the pan-
demic, statistical analyzes can be used to predict the number
of people affected.
We used machine learning models such as the Time Series
model ARIMA and FBProphet to predict the daily recorded
cases (Confirmed, Recovered, and Deaths) of COVID-19 dur-
ing this study to get an idea of the possible scenarios soon to
take the necessary measures and educate people and decision-
makers about the consequences of this epidemic.

1.3. Related Works

Works can also be found on the application of SIR (Sus-
ceptible Infectious Recovered) models [12], this last study con-
ducted by scientists revealed that the SIR model can predict the
epidemic trend based on the diseases spread.

Different diseases, such as SARS, Ebola, pandemic in-
fluenza and dengue fever, are usually predicted using time se-
ries analyses. A. G. Amiranashvili [13] conducted research
using the ARIMA model to project the number of COVID-19
cases in India in the future, and they determined that cases in
India would increase exponentially, with cases decreasing rela-
tive to expected forecasts. They have also emphasized the im-
portance of social separation and sanitizing to reduce the spread
of the human virus. The goal of their research was to assist the
government and medical community in preparing for the crisis.
They failed to account for the effect of COVID-19 tests on the
number of cases where positive results were recorded.

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Chouit et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 843 3

A similar study was conducted by Tyagi [14], who es-
timated the number of COVID-19 cases in India using the
ARIMA model. These efforts are directed toward the goal of
developing effective methods to reduce the impact of infectious
diseases on public health. These include mean absolute error
(MAE), root mean square error (RMSE) and coefficient of de-
termination (R2).

According to Dost Khan [15] and T. Latunde [16], they
represent the popular autoregressive integrated moving aver-
age (ARIMA) will be used to forecast the cumulative number
of confirmed, recovered cases, and the number of deaths from
COVID-19 for the specific country.

Further, Miksic [17] presents the model that method con-
siders expected recoveries and deaths, and it determines maxi-
mally allowed daily growth rates that lead away from exponen-
tial increase toward stable and declining numbers.

Modelling and forecasting the total number of cases and
deaths due to the pandemic is represented by N. Khan [18] with
the use of a polynomial regression model. With data intelli-
gence, the model should be able to assess the probability of
pandemic disease.

According to Abonazel [19] used the ARIMA model based
on the Box-Jenkins approach was used to predict the confirmed,
recovered cases and deaths of COVID-19 in Egypt. Further, he
researches suitable statistical prediction models to be meaning-
ful in forecasting and controlling this global pandemic threat,
especially after the genetic mutation of the virus in 2021. The
results indicated that the estimated ARIMA models have a high
ability to predict the number of confirmed cases, recovered
COVID-19 cases and death in Egypt.

On the other side, F. O. Awedaa [20] and Awwad [21], used
two models that are Autoregressive Integrated Moving Aver-
age (ARIMA) model and the Spatial Time-Autoregressive In-
tegrated Moving Average (STARIMA) model the measure the
impact of the curfew on the spread of COVID-19 in KSA. He
concluded that STARIMA models are more reliable in forecast-
ing future epidemics of COVID-19 than ARIMA models due to
the inclusion of spatial information i.e. neighboring effect in
the form of the spatial weight matrix. Unlike the univariate
ARIMA models, STARIMA models have a smaller number of
parameters. For the illustrated dataset, the STARIMA model
has only three parameters for the three locations, whereas
ARIMA model has numerous parameters, under such cases pa-
rameterization may prompt to lower sum of squares of residu-
als. In conclusion, they studied the trend pattern of the COVID-
19 outbreak in Makkah, Jeddah, and Taif in Saudi Arabia. Fi-
nally, they found out that the best prediction model for forecast-
ing the trend of daily confirmed cases in Makkah, Jeddah, and
Taif is STARIMA.

2. Method of Data Analysis

A time series model is used to predict future outcomes
based on historical data. For our study, we used the ARIMA
and Facebook Prophet models.

2.1. Autoregressive Integrated Moving Average (ARIMA)

ARIMA(p,d,q) is composite of Autoregressive (AR) model,
Moving Average (MA) model, and ’I’ stands for integration;
where p is order of autoregression, d is order of differencing, q
is order of moving average. The AR(p) model is defined as a
linear process given as the following equation.

zt = α + φ1zt−1 + φ2zt−2 + · · · + φpzt−p + wt, (1)

where zt−1, zt−2, . . . , zt−p are the lags (past values); and
φ1,φ2, . . . ,φp are lag coefficients which are estimated by the
model, wt is the white noise, and is defined as follows.

α =

1 − p∑
i=1

φi

µ, (2)
where µ is mean of the process. The MA(q) model is defined as
the following equation.

zt = α + wt + θ1wt−1 + θ2wt−2 + · · · + θqwt−p, (3)

where wt, wt−1, . . . , wt − q error terms of the model for the re-
spective lags i.e. zt−1, zt−2, . . . , zt−p

ARIMA is able to fit if the data is stationary i.e. data mean
and standard deviation is constant. The differencing parameter
d is the order of transformation to make dataset stationary.
Second order differencing is shown in the following equation.

zt = (Zt − Zt−1) − (Zt−1 − Zt−2) = Zt − 2Zt−1 + Zt−2 (4)

Finally the equation for the ARIMA(p,d,q) is defined as fol-
lows.

zt = α +
p∑

i=1

φizt−i + wt +
q∑

j=1

θ jwt− j (5)

2.2. Facebook Prophet

Taylor et al. [11] presented the Facebook Prophet
(FBProphet), a model that combines many non-linear and lin-
ear approaches with time as a regressor. Prophet was developed
by Facebook’s data science team and released as open-source
library. The model assumes that the data’s time dependency
is not significant, and that training is treated as if it’s a curve-
fitting operation. As a result, irregular observations are per-
mitted in a dataset. The approach has few benefits, including
the ability to handle several periods of seasonality, as well as
unique and well-known holidays; it provides flexibility by of-
fering two options for trend:

1. a piecewise linear model,
2. a saturating growth model; and the model fits very fast.

The model includes another vacation term as part of the time
series, so that a time series can be defined by the following
equation.

zt = Tt + S t + Ht + �t, (6)

where Tt is trend, S t is seasonality, Ht is holiday, and �t is error
term.

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Chouit et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 843 4

3. Methods and analyzes

To predict Coronavirus disease (COIVD-19), we are using
the Time Series Analysis using Facebook prophet Model and
ARIMA, designed for making forecasts for time series datasets
and using forecasting tools available in Python. This enables us
to observe and predict the spread of the coronavirus pandemic
on a daily or weekly basis.

Then we started with reading the whole dataset and cleaning
it of missing values and outliers. We then analysis the data
into five Python libraries are ‘numpy’, ‘Pandas’, ‘Matplotlib’,
‘Datetime’, and ‘sklearn.metrics’.

3.1. Modeling Dataset
The Repository by the Center for Systems Science and En-

gineering [22] provided the data on which the resulting model
was built and tested. From March 02, 2020 to August 04, 2021,
this data shows the daily values since the first case of COVID-
19 in Morocco. Figure 1 depicts the daily cumulative con-
firmed, fatalities, and recovered cases from COVID-19 in Mo-
rocco during this time period. The total number of confirmed

Figure 1. Daily cumulative confirmed, deaths and recovered cases COVID-19
in Morocco.

cases in the dataset was 523 620, the number of recovered cases
was 510 958, and the number of death cases was 9 207 for Mo-
rocco as a whole, as shown in Table 1

Table 1. Number of COVID-19 cases in Morocco till 04 August 2021.
Confirmed Recovered Deaths

Total 653,286 582,692 10,015

our study is based on a well-structured approach to predic-
tion: analysis, cleaning and configuration of data, then predic-
tion. all the steps are mentioned in figure 2.

Figure 2 depicts the basic phases involved in the prediction
process.

The architecture used to predict and analyze instances of
COVID 19 using the ARIMA and FBProphet models is shown
in Figure 3. The confirmed, recovered, and dying datasets pa-
tients were separated into training (80%) and testing (20%)
groups (the remaining 20%).

Figure 2. Prediction process diagram.

Figure 3. Framework to evaluate the forecasting models.

3.2. Performance Measures

The following statistical metrics are used to evaluate the
prediction models:

• Mean Absolute Error (MAE):

MAE =
1
N

N∑
i=1

|yi − ŷ| , (7)

where,
ŷ predicted value of y
yi mean value of y

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Chouit et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 843 5

• Root Mean Square Error (RMSE):

MS E =
1
N

N∑
i=1

(yi − ŷ)
2 (8)

• R-Squared Score:

RMS E =
√

MS E =

√√√
1
N

N∑
i=1

(yi − ŷ)
2 (9)

4. Results and discussion

In Python 3.8, we employed the ARIMA and Prophet mod-
els from the stats-models and FbProphet open-source packages,
respectively. Our testing were performed on a machine with a
2.80 GHz Intel R© Core i7 processor, 16 GB of RAM, and an
8GB Intel R© HD Graphics 620 GPU. We’ll discuss how accu-
rate adopted models are in forecasting confirmed, recovered,
and mortality cases in this section.

4.1. Forecasting of confirmed cases
We employ ARIMA and FBProphet models to anticipate

future instances. ARIMA may be used to create predictions if
the data is stationary. The FBProphet is a tool for analyzing
real data. The forecasting accuracy statistics for verified Mo-
roccan instances are shown in Table 2. In terms of various sorts

Table 2. Table of different metrics (Confirmed Cases).
MAE RMSE R2

ARIMA 2,761.00 3,096.79 0.76
FBProphet 638.287 1,157.446 1.00

of error metrics, such as MAE, RMSE, and R2, the findings
clearly show that FBProphet performs considerably better than
the ARIMA model.

Figure 4 displays both the actual and predicted confirmed
cases. It is obvious from the graph that the predicted cumulative
confirmed cases are rising more quickly, but the curve still has
to be flattened.

Figure 4. FBProphet Forecasting for Morocco confirmed cases.

4.2. Forecasting of recovered cases
We performed an evaluation of the suggested models using

data from cured cases in Morocco to predict and assess the cure
rate of the disease. We have applied FBProphet directly on ac-
tual data to fit the model and generated the forecasting results.
The accuracy results of the models for the recovered cases are
shown in Table 3. Best results are by FBProphet. The results

Table 3. Table of different metrics (Recovered Cases).
MAE RMSE R2

ARIMA 3,301.43 3,882.269 0.72
FBProphet 474.333 803.590 1.00

reveal that FBProphet predicts values that are almost identical
to the actual values, however ARIMA does not.

Figure 5 also shows actual and anticipated recovered cases,
which demonstrates that COVID-19 recovered cases will rise
over the next few days.

Figure 5. FBProphet Forecasting for Morocco recovered cases.

4.3. Forecasting of death cases
Since the Coronavirus has caused many deaths, it’s impor-

tant to study its mortality rate to predict its future cases. This
can help plan governments for its spread. We assessed the pre-
dicting models for death cases in Morocco in this area. Table 4
indicates the models’ accuracy in predicting fatality cases. We

Table 4. Table of different metrics (Death Cases).
MAE RMSE R2

ARIMA 20.83 25.41 0.67
FBProphet 7.359 11.364 1.00

can observe that the ARIMA prediction errors are quite high,
but the prediction results of FbProphet have a low error factor.
According to the findings, FbProphet may be used to predict
occurrences in real time and structure services accordingly.

Figure 6 also displays the actual and anticipated mortal-
ity cases, which demonstrates that the COVID-19 death rate is
lower than the virus’s propagation.

Aside from predicting, some statistical and mathematical
modeling studies have been available since the beginning of the

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Chouit et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 843 6

Figure 6. FBProphet Forecasting for Morocco death cases.

COVID-19 pandemic to anticipate national and worldwide epi-
demics by varying the degrees of accuracy. The accuracy of
predictions is subject to the assumptions that have been made
based on the available data. Because of the differences in input
value parameters and assumptions, the forecasting results may
vary. During new pandemics, such as COVID-19, the quality
and availability of information vary as the epidemic progresses,
causing uncertainty in early-stage forecasts and improving as
the epidemic progresses.

5. Conclusion

The patterns from the current data reveal that prompt and ef-
fective measures taken by Moroccan authorities to contain the
pandemic are showing a positive impact when compared with
other countries. Based on the obtained data, we predicted the
evolution of COVID-19 using the ARIMA and Prophet Mod-
els. Although the results generated by these models are quanti-
tatively different, both models have predicted an important in-
crease in confirmed cases and deaths in Morocco in the next
few months.

The finding indicates that the transmission of COVID-19
in Morocco will continue to expand in the next few months.
Fortunately, both models predict that the situation will not be
considered too dramatic and, therefore, may give hope for con-
trolling the disease. Finally, we hope that these results will help
the Moroccan government to better control and prevent the out-
break of COVID-19 in the future. The COVID-19 virus has
taken hold of the planet and has had an impact on human life
all around the world.

As a result, early prediction of the coronavirus spread can
aid in the preparation of critical government and public activ-
ities. We have demonstrated the prediction of COVID-19 by
utilizing a time series data approach based on the currently pre-
sented dataset to evaluate the COVID-19 viral outbreak data in
this paper.

This paper proposed the use of data analysis for predicting
the pandemic. It shows that the process of predicting a flu pan-
demic can be very challenging and time-consuming if the data
sources are not accurate. As a result, when using the model to
anticipate 52 weeks, this type of limitation is encountered. As

an example, an effort may be made to construct a hybrid quality
model for improved forecasting by merging the model used in
the study with other time series forecasting methods.

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