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A Mathematical Model for the Transmission of

Corona Virus Disease (COVID-19) in Sudan

Sara M-A. S. Elsheikh1, Mohamed K. Abbas2, Mohamed A. Bakheet3, and A. M. Degoot1,4∗

1Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Khartoum.

2Institute of Endemic Diseases, University of Khartoum.

3Faculty of Mathematical Sciences and Statistics, Al-Neelain University.

4African Institute for Mathematical Sciences (AIMS) Rwanda.

May 31, 2020

Abstract

The currently circulating Corona virus disease 2019 (COVID-19) has reached Sudan by the

mid of March 2020 or earlier. From the onset of the virus arrival, the government of Sudan has

allocated isolation centres across the country and gradually placed a series of measurements

to prevent the disease from widespread community transition. In this study, we present a

mathematical model for the transmission of COVID-19 in Sudan, using a modiﬁed version of

SEI framework. We placed emphasis on quantifying the impact of the control measurements

and the rate of case detecting in slowing down the spread of the disease. The model estimated

that the current case detection ratio is only 22.7%, the impact of the non-pharmaceutical in-

terventions is about 23.1% and, alarmingly, the death rate of the undetected cases is higher

than that of the detected ones. The models also showed the undetected infectious individ-

uals spread the disease more than the detected ones, i.e, the major source of the disease

transmission, an indication for the importance of social distancing. If the country continued

with the present level of measurements, the model predicted that the peak of the cumulative

Corresponding author. E-mail: abdelnasirdogooot@gmail.com.

1

detected cases would be around mid July with more than 150 thousands cases. Such ﬁgure

far exceeds the limited capacity of country’s health system. Therefore, a diverse and compre-

hensive strategies are required to improve the eﬃciency of the current measurements. Both

the control measurements and eﬀorts of case detecting are important to ﬂatten the curve, and

the best way to control the spread of the epidemic is simultaneous increase of both elements.

1 Introduction

The currently circulating Corona virus disease 2019 (COVID-19) has reached Sudan on mid

of March 2020 or earlier, and by the end of May 2020 there are 5026 conﬁrmed cases and

286 deaths. It is likely that there are far more numbers of undetected cases due to, in

part, limited number of tests, public hesitancy to report and, even worse, denial attitudes.

From the onset of the virus arrival, the government of Sudan has allocated isolation centres

across the country and gradually placed a series of measurements to prevent the disease from

widespread community transition. Started with closure of schools and universities in 13th of

March, to partial lockdown in 23th of March, and ﬁnally complete lockdown and intensiﬁed

media campaign about social distancing in 13th of April. The outbreak is continuously

growing at a steady step, posing a huge burden on public health and socioeconomic crisis

that aﬀect the daily lives of millions of people. The re-opening date is set to be in early June,

but with the daily new conﬁrmed cases in rise it is likely to be extended further.

Mathematical models proved to play a signiﬁcant role in the study of infectious diseases

[1, 2]. They can provide a deeper insight into the dynamics of the spread of diseases and also

suggest eﬀective controlling strategies to help local public health authorities in the process

of control and decisions making [3]. In this study, we present a conceivable mathematical

model for the transmission of COVID-19 in Sudan. We placed emphasis on quantifying the

impact of control measurements and the rate of case detecting in slowing down the spread

of the disease. The control measurements include government actions, public reactions and

other measures, whereby collectively we term them as “non pharmaceutical interventions” or

simply “interventions”.

Our model adopts the generic framework of SEI models and incorporates the eﬀect of

interventions through a multi-valued parameter, a step-wise constant varying during diﬀerent

2

phases of the interventions, designed to capture their impact in the model. We proof that

our model is mathematically consistent and present various simulation results using best

estimated parameters value. The model can be easily updated later when things have altered,

for example easing some restrictions, to accommodate such new changes. We hope that our

simulation results may guide the local authorities to make timely right decisions.

2 Description of the Model

We consider a modiﬁed version of Susceptible-Exposed-Infectious (SEI) model [4, 5] for

Corona virus transmission in a population of size Nindividuals. In this model, we divide the

total population into four sub-classes, susceptible “S”, exposed “E”, undetected infectious

“Iu” and detected infectious “Id”, i.e, reported to the authorities. The latter class includes

those who went under self quarantine or hospitalized, and N(t) = S(t) + E(t) + Iu(t) + Id(t).

We assume that a susceptible individual acquires infection of corona virus following ef-

fective contact with an infected individual at a rate λand moves to the exposed class E.

An exposed individual becomes infectious at a rate σand joins the undetected class Iu. The

authorities identify an individual as detected infection at a rate αfrom the undetected class.

Both the detected and undetected infectious individuals either recover from the disease at

rates γIdand γIu, respectively; or die due to the infection at rates δId,and δIu, respectively.

The parameter αcan be viewed as the rate at which an individual leaves the undetected

infectious class. Its values range from zero to one and various factors govern the value of α

such as the level of testing capacity, diagnosis rate, and public reporting.

A diagram summarising the main structure of the model is presented in Figure S1 in the

Supplementary Materials. Given the above assumptions and notations, the model is written

as follows:

˙

S(t) = −λ(t)S(t),

˙

E(t) = λ(t)S(t)−σE(t),

˙

Iu(t) = σE(t)−(α+γIu+δIu)Iu(t),

˙

Id(t) = αIu(t)−(γId+δId)Id(t),

(1)

3

subject to the initial conditions:

S(0) = S0≥0, E(0) = E0≥0, Iu(0) = Iu0≥0, Id(0) = Id0≥0.

Where λ(t) denotes the force of infection and it is given by

λ(t) = (1 −θ)β1Iu(t) + β2Id(t)

N(t),

where β1and β2are the eﬀective contact rates of an individual in the undetected and

detected infectious classes, respectively.

The values of the hyper-parameter θrange from 0 to 1, zero corresponds to the absence

of any intervention and one corresponds to a fully controlled scenario, which is an idealized

situation and nearly impossible to attain. Intermediate values of θbetween 0 and 1 quantify

the impact of the interventions. It reduces the number of susceptible individuals by factor

of (1 −θ) and we used to account for the impact of the government actions, public reactions

and other non-pharmaceutical interventions. The higher the value of θthe smaller the size

of all classes, and the smaller the value of θthe larger the size of all classes.

Similarly, for α, the ratio of detecting the infectious individuals, the higher its value the

larger the size of detected infectious class, leading to a more controlled situation, and the

smaller the value of αthe larger the size of undetected infectious and thus resulting a less

controlled situation.

3 Analysis of the model

A complete mathematical analysis of Corona virus model in (1) from various perspectives is

given in Section S2 in the Supplementary Materials. In particular, given non-negative initial

conditions, the model is well-posed, has a non-negative solution for all t > 0, and its solution

is positively-invariant in the region

Ω = (S, E, Iu, Id)∈R4

+:S+E+Iu+Id≤N0

(see the proof of Proposition S2.1). The model attains a stable disease free equilibrium

(DFE) at the point E0= (N, 0,0,0), which satisﬁes all required conditions listed in [6]. The

expression of the basic reproduction number R0, the expected number of secondary infections

4

caused by a single infection [7, 8, 9], derived from the model using the next generation matrix

approach [6] was follows

R0= (1 −θ)β1

(α+γIu+δIu)+αβ2

(α+γIu+δIu)(γId+δId).(2)

The ﬁrst and second terms in R0represent the number of new infections produced by a

typical individual during the time she/he spends in the undetected and detected infectious

class, respectively.

The DFE point E0is is locally-asymptotically stable if R0<1, and unstable if R0>1.

The model has a unique and locally-asymptotically stable endemic equilibrium whenever

R0>1 (see Lemma S2.1 and S2.2).

4 Data

Accurate statistics for the total population of Sudan are not available, however the World

Bank and other sources [10, 11, 12] estimated the population to be around 43 million people.

We used this total number to estimate the size of susceptible class at the beginning of the

epidemic, S0.

COVID-19 rarely aﬀect children and young people, therefore we excluded 40% of pop-

ulation and set S0= 0.6N. We obtained data of daily conﬁrmed cases for the COVID-19

epidemic in Sudan from [13] and through oﬃcial reports [14]. There is a rapid increase in the

daily new conﬁrmed cases (see panel aof Figure 1). The death rate is above 5%, which is

relatively high, while the recovery rate is about 28% (see panel bof Figure 1). Information

for the onset of symptoms or the duration that recovered individuals spent in isolation centres

were not available.

5 Parameter Estimation

The transmission dynamics of COVID-19 varies among diﬀerent communities; because the

contact rate depends on various factors that are unique for each particular community, such

as social behaviours and cultural norms. In addition, there are wide range variations of

parameters value for COVID-19 models developed thus far [15, 16, 17]. Therefore, we decided

5

(a) (b)

Figure 1: The cumulative number of (a) conﬁrmed and (b) deceased and recovered COVID-

19 cases in Sudan. The red dashed vertical line in panel (a) represents the beginning of the

complete lockdown.

to estimate all parameters of the model from the cumulative conﬁrmed cases, instead of relying

on known values from literature.

In the estimation process we used the least square method [18] to minimise the errors

between the observed (conﬁrmed) cases and those predicted by the model. Table 1 presents

the estimated value for each parameter, and Figure 2 shows the model ﬁt to the cumulative

conﬁrmed data.

6 Results

Figure 2 illustrates that the model ﬁts relatively well to the cumulative cases of COVID-19

in Sudan. We will use those optimally estimated parameter values to calculate the value of

R0, and make projections with the present measurements and other scenarios.

The value of R0obtained from the estimated parameters using Equation (2) was 2.50,

which is within the interval of internationally estimated range of R0values for the COVID-19

pandemic [17, 19, 20, 21]. R0is highly sensitive to the value of β1(see Section S3 in the

Supplementary Materials) indicating the importance of social distancing, and to lesser degree

to the values of β2and θ.

6

Table 1: Parameters in the model and their estimated values from the cumulative conﬁrmed

cases

Parameter Description Estimated value

β1Eﬀective contact rate for the undetected infectious class 1.86

β2Eﬀective contact rate for the detected infectious class 1.66

σ σ−1is the average of initial latent period (in days) 0.27

αCase detecting rate 0.227

θimpact of non-pharmaceutical interventions 0.231

γIuRecovery rate of undetected infectious individuals 0.48

δIuDisease-induced death rate of undetected infectious individuals 0.061

γIdRecovery rate of detected infectious individuals 0.53

δIdDisease-induced death rate of detected infectious individuals 0.053

Figure 2: Model ﬁt for the reported cumulative COVID-19 cases. The red points are the

cumulative conﬁrmed number of COVID-19 cases and continuous blue curve represents the

model ﬁt.

Also from the estimated parameter values in Table 1 we can deduce the followings. The

undetected infectious individuals spread the disease more than the detected ones; the mean

7

incubation period of the disease in the country is approximately about 4 days; case detection

ratio is 22.7% and impact of the non-pharmaceutical intervention is only 23.1%. The death

rate of the undetected cases is higher than that of the detected ones (see δIuand δIdin Table

1), but the detected cases recover faster than the undetected ones (see the inverse of γIuand

γIdin Table 1).

7 Simulating Scenarios

Having estimated values of the parameters, the model allows to consider many scenarios by

increasing and/or decreasing the value of control measurements (θ) and/or case detecting rate

(α), starting from the current situation. Continuing with the present level of measurements,

θ= 23.1% and α= 22.7%, the model predicted that the peak of the cumulative cases would

be around mid July and then the cases start to decrease. At the peak time there will be

around 390 thousands undetected cases and about 150 thousands detected cases (see panel

(a) in Figure 3). This number far exceeds the limited capacity of the fragile health system

of the country, and therefore a diverse and comprehensive strategies are required to improve

the eﬃciency of the current measurements.

(a) (b)

Figure 3: Simulation results of the model in case of (a) continuing with the current level of

measurements, the peak will be in mid July and about 150 thousands people will be detected;

and in case of (b) increasing θby 10% while maintaining the current case detecting rate, the

peak will be in early August and about a 73 thousands people will be detected.

Improving control measurements by increasing the current level of interventions (θ) by

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factor of 10% while maintaining the present case detecting rate decreases the number of

detected and undetected cases by factor of 52% and 51%, respectively, by the peak period,

which is predicted to be around early August (see panel (b) in Figure 3).

(a) (b)

Figure 4: Simulation results of the model in case of (a) increasing αby 10% while maintaining

the current level of interventions, the peak will be around mid July and about 180 thousands

people will be detected; and in case of (b) increasing both θand αby factor 10%, the peak

will be in mid August and about 85 thousands people will be detected.

But, increasing the case detecting eﬀorts by factor of 10% while keeping the current

level of restrictions, yields an increase in the number of detected cases by factor of 20% and

decreases the number of undetected infections by 17% at the peak time (see panel (a) in

Figure 4). Increasing both the case detecting rate and non pharmaceutical interventions by

factor of 10% will drastically decrease both the number of undetected individuals (about

61%) and the number of detected cases (about 44%), but will postpone the peak time, which

is projected to be after mid August (see panel (b) in Figure 4).

Considering the opposite direction, relaxing the current control measurements by factor of

10% and keeping the current level of case detecting rate would result 55% and 56% increase

in the total number of the detected and undetected cases, respectively (see panel (a) in

Figure 5). However, panel (b) in Figure 5 shows that decreasing the current level of control

measurements by factor of 10% and increasing the rate of case detecting by factor of 30%

would maintain the total number of undetected infections in vicinity of 400 thousands cases

and more than a twice the number of detected cases.

Table 2 summarizes the results of the above discussed scenarios. It presents the actual

9

(a) (b)

Figure 5: Simulation results of the model in case of (a) decreasing θby 10% while maintaining

the same case detecting; and in case of (b) decreasing θby 10% and increasing case detecting

eﬀorts by factor of 30%.

numbers of detected and undetected infected, the corresponding R0value, and the peak time

for each scenario.

%θIncrement %αIncrement Values R0# Undetected # Detected Peak period

0% 0% 2.5 390360 150845 mid July

0% 10% 2.46 325549 181180 mid July

10% 0% 2.17 189226 73309 early August

10% 10% 2.14 152348 84992 mid August

−10% 0% 3.82 607779 233962 early July

−10% 30% 2.72 391870 350282 mid July

Table 2: Simulation results of the model under diﬀerent scenarios.

8 Discussion

Mathematical models are integral part in the process of controlling the spread of infectious

diseases. They can provide a global picture of a disease dynamics in a community under diﬀer-

ent scenarios and propose the best feasible strategies to contain the epidemic. In this study,

following the generic framework of SEI models, we have presented a simple deterministic

model for transmission of COVID-19 disease in Sudan, placing emphasis on how the control

10

measurements and eﬀort of case detecting aﬀect the dynamics of the disease. We also have

proved that the model is mathematically concise and well-posed (see the Supplementary

materials).

We utilized the cumulative number of conﬁrmed cases and quantiﬁed the level of control

measurements and case detecting rate. The model estimated the current level of control

measurements to be around 23.1%, eﬀort of case detecting is about 22.7% and R0= 2.5.

If the country continues with current level of interventions, the model predicted that at the

peak time there would be around a 150 thousands cumulative cases will be detected. This

number is beyond the capacity of the country’s health system, and that the current eﬀort is

not enough to ﬂatten the curve, more robust strategies are needed.

The model shows that both the control measurements and eﬀorts of case detecting are of

equal importance to ﬂatten the curve. The best way to control the spread of the epidemic

is simultaneous increase of both elements. The model also showed that the control mea-

surements delays the peak time but lowers the overall number of infections. The opposite is

typically true -relaxing the current level of control measurements accelerates the peak period

but increases the number of infections.

The lockdown cannot be in place for so long time as it has unbearable economic burden on

the daily-based workers and informal traders. If the country had to be open, increasing the

case detecting rate by 30%, as the model predicted, may compensate the relaxation of control

measurements. This could be achieved various ways including expanding testing capacity,

active and in time contact tracing, and intense media campaign.

The model presented here endows all limitations of deterministic dynamical models. The

precise impact of control measurements is hard to quantify in exact numbers [17], and that the

transmission dynamics of a highly contagious disease like COVID-19 can not fully captured

by a mathematical model. Nonetheless results of the model are plausible to some degree

-actual ﬁgures might be diﬀerent but in general will follow similar trajectories. We hope that

authorities may consider our ﬁndings in making practical decisions.

11

Acknowledgements

This project is funded by the Ministry of Higher Education And Scientiﬁc Research of Sudan,

through a grant to the Institute of Endemic Diseases at University of Khartoum, and by

the African Institute for mathematical Sciences (AIMS)-Rwanda. We are grateful to Prof

Muntaser Ibrahim, Dr Mohamed Abdalla, and Prof Wilfred Ndifon for their advice and

encouragements. We also would like to acknowledge the invaluable conversations we had

with Dr Aymen Hussein.

Author Contributions

All authors contributed equally.

Conﬂicts of Interest

Authors declare no conﬂict of interest.

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