SEIR Model and Simulation for Controlling Malaria Diseases Transmission without Intervention Strategies

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 3 Issue 6, October 2019
@ IJTSRD | Unique Paper ID – IJTSRD25235
SEIR Model and Simulation
Diseases Transmission
College
Department of Mathematics,
ABSTRACT
In this study we have develop a basic deterministic mathematical model to
investigate SEIR Model and Simulation for controlling malaria Diseases
Transmission without Intervention Strategies. The model has seven non
linear differential equations which descr
three state variables for mosquitoes populations and four state variables
for humans population and to introduce the model without intervention
strategies.
The models are analyzed qualitatively to determine criteria for
malaria transmission, and are used to calculate the basic reproduction R_0.
The equilibria of malaria models are determined. In addition to having a
disease-free equilibrium, which is locally asymptotically stable when the
R_0<1, the basic malaria model manifest one's possession of (a quality of)
the phenomenon of backward bifurcation where a stable disease
equilibrium co-exists(at the same time) with a stable endemic equilibrium
for a certain range of associated reproduction number less t
results also designing the effects of some model parameters, the infection
rate and biting rate. The numerical analysis and numerical simulation
results of the model suggested that the most effective strategies for
controlling or eradicating the spread of malaria were suggest to use
insecticide treated bed nets, indoor residual spraying, prompt effective
diagnosis and treatment of infected individuals.
KEYWORDS: Malaria, Basic reproduction number, Stability analysis,
Existence of Backward bifurcation analysis, Endemic equilibrium point
1. INTRODUCTION
Malaria is an infectious disease and is life threatening for
human beings worldwide. Parasite is an organism that
lives on or inside a human body from which it gets its food.
Malaria is caused due to a parasite called Plasmodium.
Plasmodium parasite is transmitted into hu
when an infected female anopheles mosquito makes bites.
Plasmodium parasites making the human liver as their
home multiply their population and start infecting red
blood cells of the human. A variety of plasmodium
parasites exist. Mainly four types of plasmodium cause
malaria disease among the human viz., falciparum, vivax,
ovale and plasmodium malaria [60].
human beings having a huge social, economic, and health
burden. Malaria transmission occurs in all over the
worldwide. Globally, an estimated 3.2 billion people are at
risk of being infected with malaria and developing disease,
and 1.2 billion are at high risk (>1 in 1000 chance of
getting malaria in a year). According to the latest
estimates, 198 million cases of malaria occurred globally
in 2013 (uncertainty range 124-283 million) and the
disease led to 584,000 deaths (uncertainty range 367,000
755,000).The burden is heaviest in the WHO African
Region, where an estimated 90% of all m
2019 Available Online: www.ijtsrd.com e
25235 | Volume – 3 | Issue – 6 | September -
nd Simulation for Controlling Malaria
Diseases Transmission without Intervention Strategies
Fekadu Tadege Kobe
College of Natural and Computational Science,
Mathematics, Wachemo University, Hossana, Ethiopia
In this study we have develop a basic deterministic mathematical model to
investigate SEIR Model and Simulation for controlling malaria Diseases
Transmission without Intervention Strategies. The model has seven non-
linear differential equations which describe the spread of malaria with
populations and four state variables
for humans population and to introduce the model without intervention
The models are analyzed qualitatively to determine criteria for control of a
malaria transmission, and are used to calculate the basic reproduction R_0.
The equilibria of malaria models are determined. In addition to having a
free equilibrium, which is locally asymptotically stable when the
aria model manifest one's possession of (a quality of)
the phenomenon of backward bifurcation where a stable disease-free
exists(at the same time) with a stable endemic equilibrium
for a certain range of associated reproduction number less than one. The
results also designing the effects of some model parameters, the infection
rate and biting rate. The numerical analysis and numerical simulation
results of the model suggested that the most effective strategies for
he spread of malaria were suggest to use
insecticide treated bed nets, indoor residual spraying, prompt effective
diagnosis and treatment of infected individuals.
Malaria, Basic reproduction number, Stability analysis,
sis, Endemic equilibrium point
How to cite this paper
Kobe "SEIR Model and Simulation for
Controlling Malaria Diseases
Transmission without Intervention
Strategies" Published
in International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456
6470, Volume
Issue-6, O
2019, pp.151
https://www.ijtsrd.com/papers/ijtsrd25
235.pdf
Copyright © 2019 by author(s) and
International Journal of Trend in Scientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the Creative
Commons Attribution
License (CC BY 4.0)
(http://creativecommons.org/licenses/b
y/4.0)
fectious disease and is life threatening for
human beings worldwide. Parasite is an organism that
lives on or inside a human body from which it gets its food.
Malaria is caused due to a parasite called Plasmodium.
Plasmodium parasite is transmitted into human body
when an infected female anopheles mosquito makes bites.
Plasmodium parasites making the human liver as their
home multiply their population and start infecting red
blood cells of the human. A variety of plasmodium
s of plasmodium cause
malaria disease among the human viz., falciparum, vivax,
human beings having a huge social, economic, and health
sion occurs in all over the
worldwide. Globally, an estimated 3.2 billion people are at
risk of being infected with malaria and developing disease,
and 1.2 billion are at high risk (>1 in 1000 chance of
getting malaria in a year). According to the latest
stimates, 198 million cases of malaria occurred globally
283 million) and the
disease led to 584,000 deaths (uncertainty range 367,000-
755,000).The burden is heaviest in the WHO African
Region, where an estimated 90% of all malaria deaths
occur, and in children aged under 5 years, WHO account
for 78% of all deaths [45].
In our country Ethiopia is a major public health problem
and has been reported the last five years (2002
proportion of malaria in outpatient
and in-patient deaths has been increasing with the highest
being recorded in 2003 and 2004. In 2008 malaria was
still the first leading cause of health problem accounting
for 48% of outpatient consultations, 20% admissions and
24.9% inpatient deaths. According to FMOH reports,
approximately 70,000 people die of malaria each year in
Ethiopia [10].
Malaria is a life threatening infectious disease caused by a
parasite called Plasmodium which is transmitted through
the bites of infected female anopheles mosquitoes. There
are four different species causing the human malaria
disease plasmodium falciparum
plasmodium ovale and plasmodium malaria ([44],[50]).
The plasmodium parasite is injected into the human
bloodstream in the form or stage or life cycle known as
sporozoite. The parasites go through a complex life cycle
inside the hosting human body and they live at various
stages both in liver and red blood cells. From time to time
e-ISSN: 2456 – 6470
October 2019 Page 151
for Controlling Malaria
Intervention Strategies
Hossana, Ethiopia
How to cite this paper: Fekadu Tadege
Kobe "SEIR Model and Simulation for
Controlling Malaria Diseases
Transmission without Intervention
Strategies" Published
in International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-3 |
6, October
2019, pp.151-162, URL:
https://www.ijtsrd.com/papers/ijtsrd25
Copyright © 2019 by author(s) and
International Journal of Trend in Scientific
ch and Development Journal. This
is an Open Access article distributed
under the terms of
the Creative
Commons Attribution
License (CC BY 4.0)
http://creativecommons.org/licenses/b
occur, and in children aged under 5 years, WHO account
In our country Ethiopia is a major public health problem
and has been reported the last five years (2002-2008) the
proportion of malaria in outpatient department, admission
patient deaths has been increasing with the highest
being recorded in 2003 and 2004. In 2008 malaria was
still the first leading cause of health problem accounting
for 48% of outpatient consultations, 20% admissions and
npatient deaths. According to FMOH reports,
approximately 70,000 people die of malaria each year in
Malaria is a life threatening infectious disease caused by a
parasite called Plasmodium which is transmitted through
the bites of infected female anopheles mosquitoes. There
are four different species causing the human malaria
disease plasmodium falciparum, plasmodium vivax,
plasmodium ovale and plasmodium malaria ([44],[50]).
The plasmodium parasite is injected into the human
bloodstream in the form or stage or life cycle known as
sporozoite. The parasites go through a complex life cycle
human body and they live at various
stages both in liver and red blood cells. From time to time
IJTSRD25235

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD25235 | Volume – 3 | Issue – 6 | September - October 2019 Page 152
the parasites pass through various stages of their life cycle
and during which numerous human red blood cells are
destroyed. From the listed four plasmodium parasites in
our country Ethiopia are plasmodium falciparum and
plasmodium vivax, accounting for 60% and 40% cases
respectively present in country Ethiopia widely [11].
At this stage the disease generates and develops its
symptoms in the infected human body. Eventually, the
parasites become gametocytes which are in turn taken by
mosquitoes that bite the human host. Inside the mosquito,
the gametocytes mature, reproduce sexually, and migrate
into the mosquito's salivary glands, at which stage the life
cycle is repeated. For some species of Plasmodium, the
parasites may persist in the liver for months or years,
resulting in chronic and recurring eruptions of merozoites
that correspond to episodes of fever and sickness.
The effect of malaria disease varies with the infecting
variety species of Plasmodium and also with prior health
and immune status of the individual. Typically malaria
disease causes fever and chills together with headaches,
vomiting and diarrhea. It may also cause long-term
anemia, liver damage and neurological damage. The most
dangerous falciparum parasite can cause cerebral malaria
which causes frequently a fatal condition involving
damaging the brain and central nervous system. The
survived people from the cerebral malaria may too
experience brain damage.
Now a days, although malaria deaths do not occur as often
as previously, but still it remains a major public health
problem and it is too early to reach any firm conclusion
about the possibility of achieving MDGs, because of
resistance of the parasite to antimalarial drugs, the
complexity of disease, expensiveness of the control
program, seasonal variability nature of the disease [44].
In the recent time, significant resources and control
programs have been made available worldwide. The aim is
to reduce malaria infected cases and prevalence or gain
upper hand over the disease. Different strategies and
programs with varying effectiveness and efficiency are
being adopted to control malaria disease. Comparative
knowledge of these existing programs is necessary to
design and organize any new and useful and cost effective
procedure to control malaria epidemic ([44],[52]).
The National Strategic Plan for Malaria Control and
Prevention in Ethiopia (NSP) 2006-2010 aimed to rapidly
scale-up malaria control interventions to achieve a 50%
reduction of the malaria burden, in line with global Roll
Back Malaria (RBM) [35] partnership objectives. The
status of coverage of the major interventions was
measured in the Malaria Indicator Survey (MIS) 2007. The
MIS 2007 results show tremendous achievements by
Ethiopia’s malaria control program. Thus, between 2005
and 2007, insecticide-treated net (ITN) coverage
increased 15 fold, with ITN use by children under five
years of age and pregnant women increasing to nearly
45% in malaria-endemic areas and to over 60% in
households that owned at least one ITN. Overall, 68% of
households in malaria-endemic areas were protected by at
least one ITN and/or indoor residual spraying of
households with insecticide (IRS). It is believed that the
vector control interventions have contributed greatly to a
reduction in the burden of the disease. More than 20
million LLINs have been distributed to 10 million
households between 2005 and 2007. With respect to IRS
activities, evidence shows that 30% of IRS-targeted areas
were sprayed in 2007 and in 2008 the coverage increased
to 50%. So far, the main vector control activities
implemented in Ethiopia include IRS, LLINs and mosquito
larval source reduction.
The Malaria Vector Control Guidelines also addresses
vector control interventions found to be effective in past
decades. The insecticides commonly used in the country
include dichloro-diphenyl-trichloroethane (DDT),
Malathion and deltamethrin. Due to resistance of malaria
vectors to DDT, the use of this Insecticide for IRS has been
discontinued in 2009. Deltamethrin is currently being
used as an interim substitute insecticide for DDT in IRS
operations. However, the selection of insecticides for IRS
use in Ethiopia will be determined annually based on the
insecticide resistance pattern of the vectors and other
factors. Environmental management, supported by active
participation of the community and use of larvicides are
other preventive measures addressed in this guideline.
The guideline incorporates the three major vector control
measures, namely environmental management, IRS, and
LLINs [11].
Efforts to reduce malaria transmission have led to the
development of efficient vector control interventions,
particularly insecticide treated nets (ITNs), indoor
residual spraying(IRS),and larvicide ([53],[54]). The ITNs
include conventional nets treated with a WHO
recommended insecticide and long-lasting insecticidal
nets. Note that larva is an immature form of an insect and
larvicide is a chemical used to kill larvae. These
interventions are used in malaria endemic countries
especially those in sub-Saharan Africa and have led to
reduction in malaria morbidity and mortality
substantially. However, malaria epidemic continues to
claim hundreds of thousands of lives every year, thus
necessitating a continued control effort to fight against the
disease ([55], [56]).
Malaria has been considered as a global issue.
Epidemiologists together with other scientists invest their
efforts to understand the dynamics of malaria and to
control transmission of the disease. From interactions
with these scientists, mathematicians have developed
tools called mathematical models. These models are used
significantly and effectively for giving an insight into the
interaction between the humans and mosquito population,
the dynamics of malaria disease, control mechanisms of
malaria transmission and effectiveness of eradication
techniques.
Mathematical models are particularly helpful as they
consider and include the relative effects of various
sociological, biological and environmental factors on the
spread of disease. The models have played a very
important role in the development of malaria
epidemiology. Analysis of mathematical models is
important because they help in understanding the present
and future spreads of malaria so that suitable control
techniques can be adopted.

The SEIR is a simplest mathematical model and has four
classes or compartments Susceptible, Exposed, Infected
and Recovered. The effect of controlling technique in the
spread of malaria is analyzed. Using these notations, eight
classes of compartmental models are possible, SI, SIS, SEI,
SEIS, SIR, SIRS, SEIR and SEIRS ([14],[29],[57]). For
example, in an SEIRS model, a fraction of the susceptible
(S) population gets exposed (E) to infection, a part of
which then becomes infectious (I). Some from the I class
recover from the disease, and become part of the R class
with temporary immunity. When immunity is lost, they
become susceptible to pathogen attack again, and enter
the S class. The simulation studies of the model with
variable values of sensitive parameter of the spread of
malaria are performed and the results are incorporated.
The necessary conclusions have been drawn.
2. The SEIR Malaria Model without Intervention
Strategies
2.1. The model formulation
In this study we formulate that of similar model [58] and
[60] describing the transmission of malaria. The malaria
model divides the human population into four classes and
with assumptions about the nature and time rate of
transfer from one classes to another. We consider the total
population sizes denoted by ܰ௛(‫)ݐ‬ and ܰ௠(‫)ݐ‬ for the
human hosts and female mosquitoes, respectively. We will
use the SEIRS framework to describe a disease with
temporary immunity on recovery from infection. SEIRS
model indicates that the passage of individuals is from the
susceptible class, ܵ, to the exposed class,‫,ܧ‬ then to
infective class , ‫,ܫ‬ and finally to the recovery class, ܴ. ܵ(‫)ݐ‬
contains humans those do not have malaria disease but
are likely to be bitten by infected female anopheles
mosquitoes causing malaria parasite at time ‫,ݐ‬ or those
susceptible to the disease.
Many diseases like malaria have what is termed a latent or
exposed phase, ‫,)ݐ(ܧ‬ during which an individual is said to
be infected but not infectious. ‫)ݐ(ܫ‬ contains humans those
are already infected and got malaria disease. People come
into infected class from susceptible class. This is done
through infecting the susceptible mosquitoes. The
dynamic transmission of the malaria parasite between and
among individuals in both species is driven by the
mosquito biting habit of the humans. ܴ(‫)ݐ‬ contains the
people who recover from the malaria disease and return
to normal status of health or individuals who have
recovered from the disease. These humans cannot
transmit the infection to mosquitoes as we assume that
they have no plasmodium parasites in their bodies.
The transfer rates between the subclasses are composed
of several epidemiological parameters. we explained that
susceptible human bitten by an infectious anopheles
mosquito may become infected with a finite probability
that depends on the abundance of infectious mosquitoes
and human populations [20]. The model assumes straight
forward to the occurrence of the same kind that
susceptible individuals get infected through biting with
infected mosquitoes. The susceptible human population is
increased by recruitment (birth and immigration) at a
constant rate y.
All the recruited individuals are assumed to be naive when
they join the community. Infected immigrants are not
included because we assume that most people who are
sick will not travel. When an infectious female anopheles
mosquito bites a susceptible human, there is some
probability of transmission of infection from an infectious
mosquito to a susceptible human ࣂ࢓ࢎ.
The parasite then moves to the liver where it develops
into its next life stage. The infected person will move to the
exposed class. After a certain period of time, the parasite
(in the form of merozoites) enters the blood stream,
habitually the indication of malaria disease recognizable
when the symptoms begin on the human body. Then the
exposed individuals become infectious and progress to
infected state at a constant rate ࢼ࢓. We exclude the direct
infectious-to-susceptible recovery by assuming that the
individuals do not recover by natural immunity. This is a
true life to simplifying assumption because most people
have some period of immunity before becoming
susceptible again. After some time, individuals who have
experienced infection may recovered with natural
immunity at a constant rate t and move to the recovered
class. The recovered individuals have some immunity to
the disease and do not get clinically ill. Since disease-
induced immunity due to malaria is temporary a fraction
ϕ of individuals leave the recovered state to the
susceptible state. We make the simplifying assumptions
that there is no immigration of the recovered humans.
Humans leave the population through natural death m and
the infected humans have an additional disease-induced
death rate constant δ. The disease-induced rate is very
small in equivalent with the recovery rate.
We divide the mosquito population into three classes:
susceptible ܵ௠ exposed ‫ܧ‬௠ and infectious ‫ܫ‬௠. Female
anopheles mosquitoes (male anopheles mosquito is not
included in the model because only female mosquito bites
humans for blood meals) enter the susceptible class
through natural birth at a rate ρ. Susceptible mosquitoes
become infected by biting infectious humans at a ∅. The
parasites (in the form of gametocytes) enter the mosquito
with probability ࣂࢎ࢓, when the mosquito bites an
infectious human, and the mosquito moves from the
susceptible to the exposed class. After some period of time,
dependent on the ambient temperature and humidity, the
parasite develops into sporozoites and enters the
mosquito's salivary glands, and the mosquito progresses
at rate ࢼ࢓, from the exposed class to the infectious class.
We assume that the infective period of the vector ends
with its death, and therefore the vector does not recovered
from being infective [1]. The mosquitoes leave the
population through natural death. Its caused by natural
death rate and insecticides is ω. The rate of infection of
susceptible individual is ߙ௛, and the rate of infecting a
susceptible mosquito is ߙ௠.

Table 1 Variables of the basic malaria model
Variables Description
ܵ௛(‫)ݐ‬ Number of humans insusceptible compartment at time ‫ݐ‬
‫ܧ‬௛(‫)ݐ‬ Number of humans in exposed class at time ‫ݐ‬
‫ܫ‬௛(‫)ݐ‬ Number of humans in infected compartment at time ‫ݐ‬
ܴ(‫)ݐ‬ Number of humans in recovered compartment at time ‫ݐ‬
ܵ௠(‫)ݐ‬ Number of mosquitoes in susceptible compartment at time ‫ݐ‬
‫ܧ‬௠(‫)ݐ‬ Number of mosquitoes in exposed class at time ‫ݐ‬
‫ܫ‬௠(‫)ݐ‬ Number of mosquitoes in infected class at time ‫ݐ‬
ܰ௛(‫)ݐ‬ Total human population at time ‫ݐ‬
ܰ௠(‫)ݐ‬ Total mosquito population at time ‫ݐ‬
Table 2 Parameters and their interpretations for the malaria model
Parameter Description
ψ Natural birth rate of humans
ߩ Natural birth rate of mosquitoes
ߙ௛ Transfer rate of humans from susceptible to infected compartment
ߙ௠ Transfer rate of mosquitoes from susceptible to infected compartment
ߤ Natural death rate for humans
ߜ Death rate of humans due to disease-induced
߱ Death of mosquitoes caused by natural death rate and insecticides
ߚ௛ Transfer rate of humans from the exposed class to the infected class
t Transfer rate of humans from Infected to recovered class
߮ Transfer rate of humans from recovered to susceptible compartment
ߠ௠௛ Probability of transmission of infection from an infectious mosquito to a susceptible human
ߠ௛௠ Probability of transmission of infection from an infectious human to a susceptible mosquito
ߚ௠ Transfer rate of mosquitoes from the exposed class to the infected class
∅
Susceptible mosquitoes bite infected humans with this rate.
Also, infected mosquitoes bites susceptible humans with the same rate
Figure 1 The flow chart for transmission of malaria disease
2.2.2.2.2.2.2.2. Mathematical formulation of SEIR modelMathematical formulation of SEIR modelMathematical formulation of SEIR modelMathematical formulation of SEIR model
Applying the assumptions, definitions of compartmental
variables and parameters described in tables 1 and 2, the
system of non-linear differential equations which describe
the dynamics of malaria transmission with controlling
measures are formulated and presented in this section.

dS௛ ݀‫ݐ‬⁄ = ψ + ϕܴ − ߤܵ௛ − ߙ௛ܵ௛
݀‫ܧ‬௛ ⁄ ݀‫ݐ‬ = ߙ௛ܵ௛ + ߚ௛‫ܧ‬௛ − ߤ‫ܧ‬௛
݀I௛ dt⁄ = ߚ௛‫ܧ‬௛ − ߬I௛ − (μ + ߜ௛)I௛
݀R dt⁄ = τI୦ − φR − μR (1)
݀ܵ௠ ݀‫ ݐ‬ = ߩ − ߙ௠ܵ௠ −⁄ ߱ܵ௠
݀‫ܧ‬௠ ⁄ ݀‫ݐ‬ = ߙ௠ܵ௠ + ߚ௠‫ܧ‬௠ − ߱‫ܧ‬௠
݀I௠ ݀‫ݐ‬ = ߚ௠ ܵ௠⁄ − ߱I௠
The initial conditions of the system of equations (1) are
given by ܵ௛(0) = ܵ௛଴ ,‫ܧ‬௛(0) = ‫ܧ‬௛଴
, ‫ܫ‬௛(0) = ‫ܫ‬௛଴ ,R(0) = R଴
, ܵ௠(0) = ܵ௠଴, ‫ܧ‬௠(0) = ‫ܧ‬௠଴
and ‫ܫ‬௠(0) = ‫ܫ‬௠଴. Also, we
have used in equation (1) that ߙ௛ = (ߠ௠௛∅ I୫ ܰ௛⁄ )
and ߙ௠ = (ߠ௛௠∅ I୦ ܰ௛⁄ ). The term ߙ௛ denotes the rate at
which the susceptible humans become infected by
infectious female mosquitoes. Similarly, the term ߙ௠
denotes the rate at which the susceptible mosquitoes
become infected by infectious humans. The rate of
infection propagated to susceptible humans by infected
mosquitoes is dependent on the total number of humans.
Similarly, the rate of infection propagated to susceptible
mosquitoes by infected humans is dependent on the total
number of humans ([18], [60]).

2.3.2.3.2.3.2.3. Analysis of SEIR model (Analysis of the ModelAnalysis of SEIR model (Analysis of the ModelAnalysis of SEIR model (Analysis of the ModelAnalysis of SEIR model (Analysis of the Model
without intervention strategies)without intervention strategies)without intervention strategies)without intervention strategies)
We now analyze the SEIR model in order to show the two
controlling methods considered here have substantial
impact on controlling the transmission dynamics of
malaria disease. In fact, the disease will be completely
eradicated if the controlling methods are implemented
effectively. The two controlling mechanisms proposed
here have such a big potential. We consider now the
solutions of the system of non-linear differential equation
(1). We understand that the interpretations of these
solutions must be biologically meaningful. Hence it is easy
to identify that the feasible region of system (1) is ℝା
଻
. The
seven dimensional solution space shows that all the
solutions are positive. Hence, the feasible region
containing all the solutions of the system of equations (1)
is given by the set ߗ = ሼ(ܵ௛ , ‫ܧ‬௛, ‫ܫ‬௛ , ܴ, ܵ௠, ‫ܧ‬௠, ‫ܫ‬௠) ∈ ℝା
଻ ሽ.
Here the quantities ܵ௛ , ‫ܧ‬௛, ‫ܫ‬௛ , ܴ, ܵ௠, ‫ܧ‬௠, ‫ܫ‬௠ are all non–
negatives. Further the total human and mosquito
populations are represented by ܰ௛ and ܰ௠they have the
upper asymptotic values (ψ ߤ⁄ ) and (ߩ ߱⁄ ) respectively.
Therefore, the region ߗ is positively invariant i.e. solutions
remain positive for all the temporal values. Thus, the
model (1) is biologically meaningful and mathematical
well-posed or well present in the domain ߗ.
On summing up all the individual equations from (1) of
the system (1), it is straight forward to get (݀ܰ௛ ݀‫ݐ‬⁄ ) =
(ψ − ߤܰ௛ − ߜ௛I௛). Here the notation ܰ௛ = (ܵ௛ + ‫ܧ‬௛ + ‫ܫ‬௛ +
ܴ represents the total human population contained in all
the five compartments. We consider the solution of the
system of equations (1) when the term ߜ௛I௛ vanishes. In
case if the death rate of humans due to malaria disease is
considered to be free, i.e., ߜ௛ = 0 then we
obtain(݀ܰ௛ ݀‫ݐ‬⁄ ) = (ψ − ߤܰ௛). The solution of this
differential equation is found to be ܰ௛(‫)ݐ‬ = (ψ ߤ⁄ ) +
ሾܰ௛଴ − (ψ ߤ⁄ )ሿ ݁ିఓ௧
showing that ܰ௛(‫)ݐ‬ → ψ ߤ⁄ as ‫ݐ‬ → ∞.
The termܰ௛଴ denotes the initial total human population. It
can be interpreted that the total human population grows
and asymptotically converges to a positive quantity given
by (ψ ߤ⁄ ) under the condition that humans do not die due
to malaria infection. Thus ψ ߤ⁄ is an upper bound of the
total human populationܰ௛(‫ݐ‬ ) i.e.ܰ௛(∞) ≤ ψ ߤ⁄ . Whenever
the initial human population starts off low below (ψ ߤ⁄ )
then it grows over time and finally reaches the upper
asymptotic value (ψ ߤ⁄ ). Similarly, whenever the initial
human population starts off high above (ψ ߤ⁄ ) then it
decays over time and finally reaches the lower asymptotic
value (ψ ߤ⁄ ) ሾ3ሿ.
Similarly on summing up all the individual equations from
the system (1), it is straight forward to get ݀ܰ௠ ݀‫ݐ‬⁄ = ߩ −
߱ ܰ௠. Here the notation ܰ௠ = (ܵ௠ + ‫ܧ‬௠ + ‫ܫ‬௠) represents
the total mosquito population contained in all the two
compartments. The solution of this differential equation is
found to be ܰ௠(‫)ݐ‬ = (ߩ ߱⁄ ) + ሾܰ௠଴ − (ߩ ߱⁄ )ሿ ݁ିఠ௧
showing that ܰ௠(‫)ݐ‬ → (ߩ ߱⁄ )as ‫ݐ‬ → ∞. The term
ܰ௠଴denotes the initial total mosquito population. It can be
interpreted that the total mosquito population grows and
asymptotically converges to a positive quantity given by
(ߩ ߱⁄ ).Thus (ߩ ߱⁄ ) is an upper bound of the total mosquito
populationܰ௠(‫ݐ‬ ) i.e. ܰ௠(∞) ≤ (ߩ ߱⁄ ) . Whenever the
initial mosquito population starts off low below (ߩ ߱⁄ )
then it grows over time and finally reaches the upper
asymptotic value(ߩ ߱⁄ ). Similarly, whenever the initial
mosquito population starts off high above (ߩ ߱⁄ ) then it
decays over time and finally reaches the lower asymptotic
value(ߩ ߱⁄ ) ሾ26ሿ.
Hence all feasible solutions set of the human population
and mosquito population of the model (1) enters the
region.
ߗ = ሼ(ܵ௛ , ‫ܧ‬௛, ‫ܫ‬௛ , ܴ, ܵ௠, ‫ܧ‬௠, ‫ܫ‬௠) ∈ ℝା
଻
; (ܵ௛, ܵ௠) ≥
0,‫ܧ‬ℎ, ‫ܫ‬ℎ , ܴ,‫,݉ܧ‬ ‫ܰ,0≥݉ܫ‬ℎ≤ψߤ,ܰ݉≤ߩ߱ .
Therefore, the region ߗ is positively invariant (i.e
solutions remain positive for all times t) and in the model
(1) is biologically meaningful and mathematically well-
posed in the domain ߗ.
3.3.3.3. Existence of Disease free equilibrium pointExistence of Disease free equilibrium pointExistence of Disease free equilibrium pointExistence of Disease free equilibrium point ࡱ࢕
Disease free equilibrium points are steady state solutions
when there is no malaria in the human population and
there is no plasmodium parasite in the mosquito
population. That is, absence of malaria causing infections
occurs in both populations at the disease free equilibrium
point. The disease free equilibrium point is denoted by
‫ܧ‬଴ = (ܵ௛
∗
, ‫ܧ‬௛
∗
, ‫ܫ‬௛
∗
, ܴ∗
, ܵ௠
∗
, ‫ܧ‬௠
∗
‫ܫ‬௠
∗ ). The equilibrium point is
obtained on setting the right-hand side of the non-linear
system (1) to zero. Thus, at the equilibrium point the
quantities satisfy the condition ‫ܧ‬௛
∗
= ‫ܫ‬௛
∗
= ܴ∗
= ‫ܧ‬௠
∗
=
‫ܫ‬௠
∗
= 0, ܵ௛
∗
= (ψ ߤ⁄ ) and ܵ௠
∗
= (ߩ ߱⁄ ).Also, Overhead star
represents the values of the functions at the disease free
equilibrium point. The disease free equilibrium point
represents ‫ܧ‬଴the disease free situation in which there is
no malaria infection either in the society or in the
environment. Therefore, the diseases free equilibrium
point is given by
‫ܧ‬଴ = (ψ ߤ⁄ , 0, 0, 0, ߩ ߱⁄ , 0 ,0) (2)
4.4.4.4. Basic Reproduction NumberBasic Reproduction NumberBasic Reproduction NumberBasic Reproduction Number ‫܀‬૙
Reproduction number, denoted by ܴ௢ , is the threshold or
a level for many epidemiological models. It determines
whether a disease can attack the population or not. The
threshold quantity ܴ଴ indicates the number of new
infected individuals is produced by one infected
individual. When ܴ଴ < 0 each infected individual
propagates the infection and produces on average less
than one new infected individual so that the disease is
expected to die out completely over time. On the other
hand if ܴ଴ > 1,each individual produces more than one
new infected individual so we would expect the disease to
spread more and grow in the population. This means that
the value of threshold quantity ܴ଴ in order to eradicate
the disease must be reduced by less than one.
The following steps are followed to compute the basic
reproduction number ܴ଴. The basic reproduction number
cannot be determined from the structure of the
mathematical model alone, but depends on the definition
of infected and uninfected compartments. Assuming that
there are ݊ compartments of which the first ݉
compartments to infected individuals. That is the
parameters may be vary compartment to compartment,
but are the identical for all individuals within a given
compartment. Let
ܺ௜ = (‫ݔ‬ଵ , ‫ݔ‬ଶ, ………. ‫ݔ‬௡), ܺ௜ ≥ 0 for all, ݅ = 1,2, … … . ݉

Be the vector of human and mosquito individuals in each
compartment. Let us sort the compartments so that first
݉ compartments infected individuals.
Let ‫ܨ‬௜(‫)ݔ‬ be the rate of appearance of new infections in
compartment ݅.
ܸ௜(‫)ݔ‬ = ܸି
௜(‫)ݔ‬ − ܸା
௜(‫)ݔ‬ Where ܸା
௜(‫)ݔ‬ is rate of transfer
of individuals into compartment ݅ by all other means and
ܸି
௜(‫)ݔ‬ is the rate of transfer of individual out of the ݅௧௛
compartment.
It is assumed that each function is continuously
differentiable at least twice in each variable. The disease
transmission model consists of non-negative initial
conditions together with the following system of
equations:
ௗ௫೔
ௗ௧
= ݂௜(‫)ݔ‬ = ‫ܨ‬௜(‫)ݔ‬ − ܸ௜(‫,)ݔ‬ ݅ = 1,2,3 … . . ݊
Where
ௗ௫೔
ௗ௧
is the rate of change of ‫.ݔ‬ The next is the
computation of the square matrices ‫ܨ‬ and ܸ of
order (݉‫,)݉ݔ‬ where ݉ is the number of infected classes,
defined by ‫ܨ‬ = ሾ
ௗி೔(௫)
ௗ௫௝
(‫ݔ‬଴)ሿ and ܸ = ሾ
ௗ௏೔(௫)
ௗ௫௜
(‫ݔ‬଴)ሿ with
1 ≤ ݅, ݆ ≤ ݉, such that ‫ܨ‬ is non- negative, ܸ is non-singular
matrix and ‫ݔ‬଴ is the disease-free equilibrium point (DFE).
Since ‫ܨ‬ is non-negative and ܸ is non-singular, then ܸିଵ
is
non-negative and also ‫ܸܨ‬ିଵ
is non-negative. Hence the of
‫ܸܨ‬ିଵ
is called the next generation matrix for the model.
Finally the basic reproduction number ܴ଴ is given by
ܴ଴ = ߛ(‫ܸܨ‬ିଵ
)
Where ߛ(‫)ܣ‬ denotes the spectral radius of matrix ‫ܣ‬ and
and the spectral radius is the biggest non-negative
eigenvalue of the next generation matrix. Rewriting model
system (1) starting with the infected compartments for
both populations; ܵ௛ , ‫ܧ‬௛, ‫ܫ‬௛ , ܴ, ܵ௠, ‫ܧ‬௠, ‫ܫ‬௠ and then
following by uninfected classes; ܵ௛ , ܴ, ܵ௠ also from the
two populations, then the model system becomes
݀‫ܧ‬௛
݀‫ݐ‬
ൗ =
ߠ௠௛∅ I୫ܵ௛
ܰ௛
− (ߚ௛ + ߤ)‫ܧ‬௛
݀I௛ dt⁄ = ߚ௛‫ܧ‬௛ − (߬ + μ + ߜ௛)I௛
݀‫ܧ‬௠
݀‫ݐ‬
ൗ =
ߠ௛௠∅ I୦ܵ௠
ܰ௛
− (ߚ௠ + ߱)‫ܧ‬௠
݀I௠ ݀‫ݐ‬ = ߚ௠ ܵ௠⁄ − ߱I௠ (3)
dS௛ ݀‫ݐ‬⁄ = ψ + ϕܴ −
ߠ௠௛∅ I୫ܵ௛
ܰ௛
− ߤܵ௛
݀R dt⁄ = τI୦ − (φ + μ)R
݀ܵ௠ ݀‫ݐ‬ = ߩ −
ߠ௛௠∅ I୦ܵ௠
ܰ௛
−ൗ ߱ܵ௠
Since ߙ௛ = ቀ
ఏ೓೘∅ ୍౞
ே೓
ቁ and ߙ௠ = ቀ
ఏ೘೓∅ ୍౞
ே೓
ቁ (1) malaria
model. From the system of equation (3) ‫ܨ‬௜ and ܸ௜ are
defined as
‫)ݔ(ܨ‬ =
‫ۏ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ۍ‬
ߠ௠௛∅ I୫ܵ௛
ܰ௛
0
ߠ௠௛∅ I୫ܵ௛
ܰ௛
0 ‫ے‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ې‬
ܸ(‫)ݔ‬ = ൦
(ߚ௛ + ߤ)‫ܧ‬௛
(߬ + μ + ߜ௛)I௛ − ߚ௛‫ܧ‬௛
(ߚ௠ + ߱)‫ܧ‬௠
ߚ௠ ܵ௠ − ߱I௠
൪
The partial derivatives of (3) with respect to ( ‫ܫ‬௛, ‫ܫ‬௠) and the jacobian matrix of ‫ܨ‬௜ at the disease-free equilibrium point
(2) is:-
‫ܨ‬ =
‫ۏ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ۍ‬
0 0 0 ߠ௠௛∅ I୫
0 0 0 0
0
ߠ௛௠∅μρ
߱߰
0 0
0 0 0 0 ‫ے‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ې‬
Similarly, the partial derivatives of (3) with respect to (‫ܧ‬௛, ‫ܫ‬௛, ‫ܧ‬௠, ‫ܫ‬௠) and the jacobian matrix ܸ௜is:-
‫ݒ‬ = ൦
(ߚ௛ + ߤ) 0 0 0
−ߚ௛ (߬ + μ + ߜ௛) 0
0 0 0 0
0 0 −ߚ௠ ߱
൪
ܸିଵ
=
‫ۏ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ۍ‬
1
(ߚ௛ + ߤ)
0 0 0
1
(߬ + μ + ߜ௛)(ߚ௛ + ߤ)
1
(߬ + μ + ߜ௛)
0 0
0 0
1
(ߚ௛ + ߤ)
0
0 0
ߚ௠
߱(ߚ௛ + ߱)
1
߱ ‫ے‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ې‬
‫ܸܨ‬ିଵ
=
‫ۏ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ۍ‬ 0 0 0
ߚ௠ ߠ௠௛∅
߱(ߚ௛ + ߱)
1
߱
0 0 0
ߚ௛ߠ௛௠∅μρ
߱߰(߬ + μ + ߜ௛)(ߚ௛ + ߤ)
ߠ௛௠∅μρ
(߬ + μ + ߜ௛)
0 0
0 0
ߚ௠
߱(ߚ௛ + ߱)
0
‫ے‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ې‬

From ‫ܸܨ‬ିଵ
,we can determine the eigenvalues of the basic reproduction number ܴ଴ by taking the spectral radius
(dominant eigenvalue) of the matrix ‫ܸܨ‬ିଵ
. Thus it is calculated by |‫ܣ‬ − ߣ‫|ܣ‬ = 0. We determine the expression for ܴ଴ using
the next generation matrix approach ሾ18ሿ as ܴ଴ = ට
ఘ ఏ೘೓ ఏ೓೘ ∅మఓ
ఠమψ ( ఛାఒାஜ೓ାఋ೓)
. Further, it can be verified that the disease free
equilibrium point ‫ܧ‬଴ given by (4) is locally asymptotically stable if ܴ଴ < 1 and unstable if ܴ଴ > 1.
Local Stability of the DiseaseLocal Stability of the DiseaseLocal Stability of the DiseaseLocal Stability of the Disease----Free EquilibriumFree EquilibriumFree EquilibriumFree Equilibrium
The local stability of the disease-free equilibrium can be analyzed using the Jacobian matrix of the malaria model (1) at the
disease free equilibrium point. Using ሾ40ሿ, the following theorem contains
Theorem:Theorem:Theorem:Theorem:---- The disease free equilibrium point for system (1) is locally asymptotically stable if ܴ଴ < 1 and unstable if
ܴ଴ > 1
Proof:Proof:Proof:Proof: The Jacobian matrix (‫)ܬ‬ of the malaria model (1) with
ܵ௛ = (‫ܧ‬௛ + ‫ܫ‬௛ + ܴ & ܵ௠ = (‫ܧ‬௠ + ‫ܫ‬௠) at the disease-free equilibrium point is given by:
݀‫ܧ‬௛
݀‫ݐ‬
ൗ =
ߠ௠௛∅ I୫ܵ௛
ܰ௛
− (ߚ௛ + ߤ)‫ܧ‬௛
݀I௛ dt⁄ = ߚ௛‫ܧ‬௛ − (߬ + μ + ߜ௛)I௛
݀R dt⁄ = τI୦ − (φ + μ)R
݀‫ܧ‬௠
݀‫ݐ‬
ൗ =
ߠ௛௠∅ I୦ܵ௠
ܰ௛
− (ߚ௠ + ߱)‫ܧ‬௠
݀I௠ ݀‫ݐ‬ = ߚ௠ ܵ௠⁄ − ߱I௠ (4)
From the equation (4) the jacobian matrix
(‫)ܬ‬ =
‫ۏ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ۍ‬
−(ߚ௛ + ߤ) 0 0 0 ߠ௠௛∅
ߚ௛ − (߬ + μ + ߜ௛) 0 0 0
0 ߬ − (߮ + ߱) 0 0
0
ߠ௛௠∅μρ
߱߰
0 −(ߚ௠ + ߱) 0
0 0 0 ߚ௠ − ߱ ‫ے‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ې‬
Let ܽ = (ߚ௛ + ߤ) , ܾ = ߠ௠௛∅, ܿ = ߚ௛ , ݀ = (߬ + μ + ߜ௛) , ݁ = ߬ , ݂ = (߮ + ߱), ݃ =
ఏ೓೘∅ஜ஡
ఠట
, ℎ = (ߚ௠ + ߱), ݅ =
ߚ௠ ܽ݊݀ ݆ = ߱
Thus
(‫)ܬ‬ =
‫ۏ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ۍ‬
−ܽ 0 0 0 ܾ
ܿ ݀ 0 0 0
0 ݁ − ݂ 0 0
0 ݃ 0 −ℎ 0
0 0 0 ݅ − ݆ ‫ے‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ې‬
The eigenvalues of jacobian matrix are:-
|‫ܬ‬ − ߣ‫|ܫ‬ = 0
(‫)ܬ‬ =
‫ۏ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ێ‬
‫ۍ‬
−(ܽ + ߣ) 0 0 0 ܾ
ܿ ݀ 0 0 0
0 ݁ − ݂ 0 0
0 ݃ 0 −(ℎ + ߣ) 0
0 0 0 ݅ − (݆ + ߣ) ‫ے‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ۑ‬
‫ې‬
The third column has diagonal entry, therefore one of the eigenvalues of the jacobian matrix is−(߮ + ߱) or݂. By using
Routh-Hurwitz, stability criterion.
ܴ଴
ଶ
=
ఘ ఏ೘೓ ఏ೓೘ ∅మఓ
ఠమψ ( ఛାఒାஜ೓ାఋ೓)
the proof end.
Using the Routh-Hurwitz criterion is a method for determining whether a linear system is stable or be examining the
locations of the characteristic equation of the system. In fact, the method determines only if there are roots that put
outside of the left half plane; it does not actually compute the roots Routh-Hurwitz criteria [12].
We can determine whether this system is stable or not, checking the following conditions:- Two necessary but not
sufficient conditions that all the roots have negative real parts are
All the polynomial coefficients must have the same sign.
All the polynomial coefficients must be nonzero.

The necessary condition that all roots have negative real
parts is that all the elements of the first column of the
array have the same sign. The number of changes of sign
equals the number of roots with positive real parts. All the
elements of a particular row are zero. In this case, some of
the roots of the polynomial are located symmetrically
about the origin of the ߣ plane, e.g.,a pair of purely
imaginary roots. The zero row will always occur in a row
associated with an odd power of ߣ. The row just above the
zero row holds the coefficients of the auxiliary polynomial.
The roots of the auxiliary polynomial are the
symmetrically placed roots. Be careful to remember that
the coefficients in the array skip powers of ߣ from one
coefficient to the [2].
5. Existence of Backward Bifurcation
We intend to determine the stability of the endemic
equilibrium and to carry out the possibility of the
existence of backward bifurcation due to existence of
multiple equilibrium and reinfection. As a disease attacks
it reduces the number of susceptible individuals in the
population, which tends to reduce its reproductive rate.
For a backward bifurcation to occur, this means that when
ܴ଴ < 0 the endemic equilibrium point can exist as well as
when ܴ଴ > 1 .
we would expect the disease to be able to attack at ܴ଴ =
1 in the case of a backward bifurcation with the properties
of unstable equilibrium bifurcating from the disease-free
equilibrium when ܴ଴ < 1 ,giving rise to multiple stable
states. But not in the case of a forward bifurcation, in
which in the absence of a low-level unstable equilibrium
when ܴ଴ < 1 and a stable equilibrium bifurcating from the
disease-free equilibrium when ܴ଴ > 1 arise naturally
when the disease does not attack when ܴ଴ = 1 . A simple
criterion for a backward bifurcation, then, is one in which
the disease can attack when ܴ଴ = 1. This implies that the
disease-free equilibrium may not be globally
asymptotically stable even if ܴ଴ < 1 .
6. Numerical simulation
In this section we consider the simulation study of the
system of differential equations given in (1). As stated
earlier these equations describe the dynamics of human
and mosquito populations of the malaria model that
includes intervention strategies. The simulation study is
performed using ode45 solver of MATLAB software. The
Runge – Kutta fourth-order method based on a variable
step-size is used for the purpose. The parametric values
have been collected from the literature and used here.
Those were not available were not obtained from
literatures published by researchers in malaria endemic
countries which have similar environmental conditions.
we present the numerical analysis of the model(1). The
initial conditions used were ܵ௛(0) = 47186 , ‫ܧ‬௛(0) =
16987, ‫ܫ‬௛(0) = 47473, ܴ(0) = 47470, ܵ௠(0) =
17500, ‫ܧ‬௠(0) = 8750, ‫ܫ‬ெ(0) = 26,250. We simulate the
basic malaria model in the absence of any intervention and
the malaria model without intervention strategies, and
find out the effects of varying each intervention parameter
[60],[59].
6.1. Estimation of Parameters
We estimate that it will take 3 times a day for 7 days to
recovery from malaria infection through Chemotherapy
and the incubation period of malaria in humans was
considered [9],[39]and [59].
Table 3: Estimated Parameter Value of the Malaria
Modal without Intervention Strategies and with
Intervention
6.2. The System of Human Population State Variables
of the Basic Model without Intervention
The simulation of basic model has been conducted to find
out the dynamics of the disease in the population when
there is no intervention to reduce or eradicate the disease.
In the absence of interventions strategies, the susceptible
populations in Figure 2 as shown below red colored that
the change in state variables of malaria model shows the
dynamics with time of susceptible humans with
ܴ଴ = 1.3874.
Figure 2:- A phase portrait illustrating the changes in
the four state variables of the malaria model showing
the system with time, of susceptible humans, exposed
humans, infected humans and shows the system of
recovered humans with ࡾ૙ = ૚. ૜ૡૠ૝.

This susceptible population increases then remained
constant within the three years. The Figure 2 were blue
colored showing the system with time of exposed humans
initially was high but progress it reduces as some of
population enters infections class and other recover. The
green colored also were indicated in the Figure 2 showing
the dynamics with time of infectious humans the infection
population reduced as some recovers and other die within
this three years. Finally, the yellowed color in Figure 2 the
dynamics with time of recovered humans the recovered
population initially was high as time goes by the recovered
people reduced or to goes back to susceptible class.
Figure3:-Illustrates the changes in the three state
variables of the malaria model showing the dynamics
with time, of susceptible mosquitoes, exposed
mosquitoes and infectious mosquitoes with
ࡾ૙ = ૚. ૜ૡૠ૝.
In Figure 3 shows all the three curves are decreasing as
time increases which are positive for the current
interventions in the mosquito population, but there is still
more work to be done in the human population. Therefore,
we will consider the effects of varying the main
parameters responsible control malaria after considering
malaria prevalence rate in the population now.
Prevalence the Basic malaria model without
Intervention Strategies
Prevalence is defined as the ratio of which the number of
cases of a disease in a population and with the number of
individuals in a population at a given time.
Figure 4:- Represents changes of prevalence with time
with ࡾ૙ = ૚. ૜ૡૠ૝.
The prevalence graph shows that the prevalence rate as of
now is high which confirms the Figure 2 as shown above
that there is more work to be done if we want to achieve
malaria free society, because the prevalence rate reduces
asymptotically to zero as possible to increasing the
intervention strategies time to time.
6.3. Simulation of Biting Rate of Mosquitoes on the
Basic malaria model without Intervention
Strategies
We now consider the effects of varying the main
parameters responsible for controlling the spread of
malaria disease. The values of the biting rate of
mosquitoes, transmission rate of infection from an
infectious mosquito to a susceptible human, rate of loss of
immunity for humans and the mosquito population were
reduced by constant fraction 1/8 , while the values of the
other parameters are maintained. This important to
showing the relationship of the susceptible and infected
human populations was considered.
Figure 5:-Illustrates the system of infected human
population against susceptible human population with
ࡾ૙ =0.1733
The Figure 5 with ܴ଴ =0.1733 shows that at an initial
stage, the susceptible human population was free from the
disease. The infection in the population is shown that it
increases with time when there is no intervention being
practiced. As most of the susceptible individuals were
getting infected with time, the infected individuals
increases. This is evidenced further when the parameter of
mosquito biting rate varied.
7. Conclusion and Recommendation
7.1. Conclusion
Analysis of the model showed that there exists a domain
where the model is epidemiologically and mathematically
well-posed. The important parameter in our model, the
basic reproduction number ܴ଴ as an improved control
intervention measure was computed. The model was then
qualitatively analyzed for the existence and stability of
their associated equilibria. It was proved that under the
condition that ܴ଴ < 1 the disease-free equilibrium ‫ܧ‬଴ is
locally asymptotically stable, and when ܴ଴ < 1 the
endemic equilibrium ‫ܧ‬ଵ appeared. The model exhibits the
phenomenon of backward bifurcation where a stable
disease-free equilibrium co-exists with a stable endemic
equilibrium for a certain range of associated reproduction
number less than one.

The numerical analysis of the model suggested that the
most effective strategies for controlling or eradicating the
spread of malaria disease. The use of insecticide-treated
bed nets and indoor residual spraying and prompt and
effective diagnosis and treatment of infected individuals.
This study agree [7] suggestion that the intervention using
insecticide-treated bed nets represents an excellent
example of implementing an infectious disease control
programme, and [38] study, which showed that both
regular and non-fixed spraying resulted in a significant
reduction in the overall number of mosquitoes, as well as
the number of malaria case in humans.
7.2. Recommendation
So, far no really effective vaccine has been developed
against malaria, so we cannot protect ourselves against
the disease. For many years there have been few effective
treatments for malaria, but things are getting better. In a
country like ours, drugs alone are the answer. Many
peoples live a long way from medical centers and cannot
reach them easily, and medicines can be expensive.
Mathematical modeling of spread of malaria disease can
provide understanding of underlying techniques or
strategies for the disease spread, help to pinpoint key
factors in the disease transmission process, suggest
effective controlling and prevention measures, and
provide estimate for the severity and potential scale of the
endemic and epidemic. The following recommendations
should be considered:
Individuals should be award about severity of malaria
and increasing personal protection measures are
highly recommended to prevent malaria (to control
the reproduction number ܴ଴ < 1 ).
using methods of controlling malaria must involve
controlling the Anopheles mosquitoes. Whenever
possible avoid contact with mosquitoes.
Using mosquito repellents, having screens on doors,
windows to prevent mosquitoes, wearing clothes that
protect the skin against mosquito’s long sleeves and
trousers are all effective measures that can protect
you against malaria.
Well-made insecticide-treated bed nets and indoor
residual spraying and prompt and effective diagnosis
make a big difference and they are cheap treatment
effective way to control malaria transmission.
Minimize any opportunities for the mosquito breed.
They will lay eggs in any standing water in garden
pond, old trees, flower pots, old drink cans,...etc.
Removing the mosquitoes breeding places by
removing as much as standing water as possible. The
simplest way to do this to make sure you store
rubbish out of the rain and dispose of your rubbish
properly.
Proper disposal of sewage again, managing human
waste so that foul water is not left around will reduce
the breeding places for the mosquitoes.
Biological control (where an organism that feeds on
the larva is introduced to the water) and chemical
control (pesticides or IRS) spraying on to the water
where the mosquitoes breed will kill the eggs and
larvae, this in turn reduces the number of mosquitoes
and slower the infection rate.
Because of the complications of measuring malaria at
different levels with different immunological status
prevalent in different age and gender groups, and
across different locations, some guidelines should be
developed to give researchers and health
professionals a more accurate foundation on which to
select their indicators.
There should be administration of drugs to these
malaria case encountered individuals irrespective of
whether symptoms show up or not in order to
decrease the natural birth rate of mosquito and
increase the rate mosquito using Indoor-Residuals
Sprays (IRS).
References
[1] Aron, J. L (1988), "Mathematical modeling of
immunity to malaria, Non-linearity in biology and
medicine (Los Alamos, NM, 1987)", Math. Biosc.,
Volume 90,pp385396.
[2] A. Hurwitz, “On the conditions under which an
equation has only roots with negative real parts,"
Mathematische Annelen, vol.46, pp.273-284, 1895.
[3] Birkhoff, G and Rota, G.C (1982), Ordinary Differential
Equations, Ginn.
[4] Kbenesh, Cao Yanzhao, and Kwon Hee-Dae (2009),
"Optimal Control of Vector-borne Diseases:
Treatment and Prevention", Discrete and Continuous
Dynamical Systems Series B, Volume 11, No.3, pp 1-xx.
[5] Bupa UK (2009), Malaria symptoms, causes and
treatment of disease,
http://hcd2.bupa.co.uk/factsheets/html/malariadise
ase retrieved on Friday, 19th June, 2009.
[6] Carr J (1981), Applications centre manifold theory,
Springer-Verg, New York.
[7] Chavez-Castillo C and Song B (2004), "Dynamical
models of tuberculosis and their applications", Math.
Biosci. Eng., No. 2, pp 361-404
[8] E. Danso-Addo (2009), "Mathematical Model for the
Control of Malaria", MPhil Thesis, University of Cape
Coast, Cape Coast, Ghana.
[9] Federal Democratic Republic of Ethiopia Ministry Of
Health Ethiopia National Malaria Diagnosis and
Treatment Guidelines for Health Workers in Ethiopia
2nd Edition Addis Ababa, 2004
Health Ethiopia National Malaria Indicator Survey,
Addis Ababa, 2008.
Health Ethiopia National Malaria Guideline Third
Edition Addis Ababa, 2012.
[12] Flores J. D. (2011). Math-735: Mathematical
Modelling, Routh-Hurwitz criteria. Department of
Mathematical Sciences, The University of South
Dakota, jflores@usd.edu.
[13] Garba S. M, Gumel A. B and Abu Bakar M. R (2008),
"Backward bifurcation in dengue transmission
dynamics", Math.Biosci, Volume 215, pp 11-25.
[14] G. A. Ngwa and W. S. Shu (2000), "Mathematical
Computational Modeling", Vol. 32, Pp.747-763.
[15] G. A. Ngwa, Modelling the dynamics of endemic
malaria in growing populations, Discrete Contin. Dyn.
(2004), pp.1173? 1202.

[16] Greenwood B, and Mutabingwa T (2002), Malaria in
2002, Nature, Volume 415, pp 670-672.
[17] Gimnig J, Ombok M, Kamau L, Hawley W (2003)
Characteristics of larval anopheline (Diptera:
Culicidae) habitats in Western Kenya. J. Med.
Entomol. 38: 282-288.
[18] J. Y. T. Tumwiine, Mugisha and L. S. Lubobi (2007),
"Applied Mathematics and Computation", Vol.189,
Pp. 1953-1965.
[19] K. Dietz (1993), "The Estimation of the Basic
Reproduction Number for Infectious Diseases",
Statistical Methods in Medical Research, Vol. 2,
Pp. 23?41.
[20] Killeen G. F, Mckenzie F. E, Foy B. D, Bogh C and Beier
J. C (2001), "The availability of potential hosts as a
determinant of feeding behaviors and malaria
transmission by African mosquito populations",
Trans. R. Soc. Trop. Med.Hyg., Volume 95, pp 469-47
[21] Lalloo D. G, Shingadia D, Pasvol G, Chiodini P. L,
Whitty C. J and Beeching N.J(2008), UK malaria
treatment guidelines, J. Infect 2007, Volume 54, pp
111-121.
[22] L. Perko, Differential Equations and Dynamics Systems
in Applied Mathematics, vol.7, Springer, Berlin,
Germany, 2000.1
[23] N. Chitins, J. M. Cushing and J. M. Hyman (2006),
"Bifurcation Analysis of a Mathematical Model for
Malaria Transmission", Submitted: SIAM Journal on
Applied Mathematics.
[24] N. Chitnis, J. M. Hyman and J. M. Cushing (2008),
"Determining Important Parameters in the Spread of
Malaria through the Sensitivity Analysis of a
Mathematical Model", In Preparation
[25] O. Diekmann and J. A. P. Heesterbeek (1999),
"Mathematical Epidemiology of Infectious Diseases:
Model Building, Analysis and Interpretation", Wiley-
Blackwell.
[26] Namawejje H.(2011). Modeling the Effect of Stress on
the Dynamics and Treatment of Tuberculosis.
M.Sc.(Mathematical Modelling) Dissertation,
University of Dares Salaam, Tanzania.
[27] Niger Ashrafi M and Gumel Abba B (2008),
"Mathematical Analysis of the Role of Repeated
Exposure on Malaria Transmission Dynamics",
Differential Equations and Dynamical Systems,
Volume 16, No. 3, pp 251-287.
[28] Ngwa G. A (2004), Modeling the dynamics of endemic
malaria in growing population, Discrete and
Continuous Dynamic System. Ser. B, Volume 4, pp
1173-1202.
[29] Nita. H. Shah and Jyoti Gupta (2013),"SEIR Model and
Simulation for Vector Borne Diseases", Applied
Mathematics, Vol. 4, Pp. 13-17.
[30] Martens P, Kovats R. S, Nijhof S, Vries P. de,
Livermore M. T. J, Bradley D. J, Cox J and McMichael
A. J (1999), Climate change and future populations at
risk of malaria, Global Environmental Change, Volume
9, pp S89-S107.
[31] Miranda I. Teboh-Ewungkem, Chandra N. Podder and
Abba B. Gumel (2009),"Mathematical Study of the
Role of Gametocytes and an Imperfect Vaccine on
Malaria Transmission Dynamics", Bulletin of
Mathematical Biology DOI 10,Volume 1007, No. S11,
pp 538-009-9437-3.
[32] O. J. Diekmann (1990), "Mathematical Biology",
Vol.28, Pp. 365-382.
[33] O. Diekmann, J. A. Heesterbeek, and J. A. Metz, "On the
definition and the computation of the basic
reproduction ratio R_0 in models for infectious
diseases in heterogeneous populations," Journal of
Mathematical Biology, vol.28, no.4, pp.365-
382,1990.J
[34] P. M. M. Mwamtobe (2010), "Modeling the Effects of
MultiIntervention Campaigns for the Malaria
Epidemic in Malawi", M. Sc. (Mathematical Modeling)
Dissertation, University of Dar es Salaam, Tanzania
[35] RollbackMalaria. What is malaria?
http://www.rollbackmalaria.pdf. (2010-05-10).
[36] Sandip Mandal, Ram Rup arkar and Somdatta Sinha
(2011), "Mathematical Models of Malaria A Review",
Malaria Journal, Vol. 10, Pp.
[37] Samwel Oseko Nyachae, Johana K. Sigey, Jeconiah A.
Okello, James M. Okwoyo and D. Theuri (2014),"A
Study for the Spread of Malaria in Nyamira Town -
Kenya, The SIJ Transactions on Computer Science
Engineering and its Applications (CSEA), The Standard
International Journals (The SIJ), Vol. 2, No. 3 (1), Pp.
53-60.
[38] Smith R.J and Hove-Musekwa D.S (2008),
"Determining effective spraying periods to control
malaria via indoor residual spraying in sub-Saharan
Africa", Appled Math and Deci Sc., doi:10, Volume
1155, No. 745463, pp 1-19.
[39] Turuse, E.A., et al. (2014) Determinants of Delay in
Malaria Prompt Diagnosis and Timely Treatment
among Under-Five Children in Shashogo Woreda,
Hadiya Zone, Southern Ethiopia: A Case Control
Study. Health, 6, 950-959.
[40] Van den Driessche, P., and Watmough, J. (2002).
Reproduction numbers and sub-threshold endemic
equilibria for compartmental models of disease
transmission Mathematical Biosciences, 180, 29-48
http://dx.doi.org/10.4236/health.2014.610120.
[41] WHO (1993), Implementation of the global malaria
control strategy. Reports of the World Health
Organization Study Group on The Implementation of
the Global Plan of Action for malaria control 1993-
20002, WHO Technical Report Series, Number 839.
[42] WHO (1999), The WHO Report 1999Making a
difference, Geneva.
[43] WHO/UNICEF: World malaria report 2008, Report
series: WHO/HTM/MAL/2008.1102
[44] WHO (2009), "Global Malaria Programme: Position
Statement on ITNs".
[45] World Malaria Report (2014)
(www.who.int/malaria).

[46] Fekadu Tadege et.al controlling the spread of malaria
with intervention strategies ‘the book was released
by LAP LAMBERT Academic Publishing in 2016 and
ISBN 978-3-659-77730-1.
[47] M. Gatton, W. Hogarth, A. Saul and P. Dayananda
(1996),"A Model for Predicting the Transmission
Rate of Malaria from Serological Data", Journal of
Mathematical Biology, Vol. 84, Pp. 878-888.
[48] J. Nedelman (1983), "Inoculation and Recovery Rates
in the Malaria Model of Dietz, Molineaux, and
Thomas", Mathematical Biosciences, Vol. 69, No. 2, Pp.
209-233.
[49] R. Ross (1911),"The Prevention of Malaria", John
Murray, London.
[50] WHO Global Malaria Programme (2010), World
Malaria Report 2010.
[51] Macdonald G. The epidemiology and control of
malaria. London: Oxford University Press; 1957.
[52] WHO (2007) Insecticide treated mosquito nets: a
WHO position statement. Technical report, World
Health Organization.
[53] Bayoh MN, Mathias DK, Odiere MR, Mutuku FM,
Kamau L, et al.. (2010) Anopheles gambiae: historical
population decline associated with regional
distribution of insecticide-treated bed nets in
western Nyanza province, Kenya. Malaria Journal 9.
[54] WHO (2004) Global strategic framework for
integrated vector management. Technical report,
World Health Organization, Geneva.
[55] WHO (2011) World Malaria Report. Technical report,
World Health Organization, Geneva.
[56] RBM (2013) Minutes of roll back malaria vector
control working group 8th annual meeting. Technical
report, Roll Back Malaria. Available:
http://www.rollbackmalaria.org/mechanisms/vcwg.
html.
[57] Hethcote HW. In: Frontiers in mathematical biology.
Lecture notes in Bio mathematics. Simon A Levin,
editor. Vol. 100. Springer; 1984. A thousand and one
epidemic models; pp. 504-515.
[58] Chitnis N. (2005), "Using Mathematical Models in
Controlling the Spread of Malaria", Ph.D. Thesis,
Program in Applied Mathematics, University of
Arizona, Tucson, AZ.
[59] Fekadu Tk. et al(2015) "a mathematical model to
control the spread of malaria with intervention
campaigns in Shashogo Woreda, Hadiya zone,
Southern Ethiopia," Msc unpublished Thesis, program
in computational mathematics, Hawassa university
[60] Fekadu Tadege Kobe and Purnachandra Rao Koya,
"Controlling the Spread of Malaria Disease Using
Intervention Strategies" Journal of Multidisciplinary
Engineering Science and Technology (JMEST) ISSN:
3159-0040 Vol. 2 Issue 5, May 2015, pp 1068-1074.
www.jmest.org JMESTN4235074567

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