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<title>SUBHARMONIC BIFURCATION IN MALARIA-LASSA FEVER CO-INFECTION EPIDEMIC MODEL WITH OPTIMAL CONTROL APPLICATION</title>
<link>http://hdl.handle.net/123456789/1622</link>
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<dc:date>2026-04-04T11:59:17Z</dc:date>
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<title>SUBHARMONIC BIFURCATION IN MALARIA-LASSA FEVER CO-INFECTION EPIDEMIC MODEL WITH OPTIMAL CONTROL APPLICATION</title>
<link>http://hdl.handle.net/123456789/1623</link>
<description>SUBHARMONIC BIFURCATION IN MALARIA-LASSA FEVER CO-INFECTION EPIDEMIC MODEL WITH OPTIMAL CONTROL APPLICATION
ONIFADE, Akindele Akano
Co-infection with malaria often complicate and increase the severity of disease&#13;
pathogenesis. The co-infection of malaria and Lassa fever has not been fully&#13;
understood. Many researcher have worked on mathematical models describing&#13;
the features involved in the transmission of mono-infection of malaria and Lassa&#13;
fever. However, models on co-infection that incorporate seasonal variation of vec tors needed for a full understanding and management of the co-infection in human&#13;
with plasmodium falciparum and Lassa virus are sparse. Therefore, this study was&#13;
designed to develop a mathematical model that incorporates seasonal variation of&#13;
vectors and investigate the e ect of endemic malaria mortality rate of Lassa fever&#13;
patients.&#13;
A co-infection mathematical model governed by a system of ordinary di eren tial equations that incorporates seasonal variation of vectors, ξm, diagnostic factor&#13;
for treatment, ηv, treatment rate, σ, biting rate, b, contact rate, w1, proportion of&#13;
e ective treatment, γv and force of infection, φ0 was formulated using law of mass&#13;
action. The state variables Nes, Nis and Nrs denoted the number of human pop ulation that were exposed, infected and recovered from Lassa fever, respectively,&#13;
but susceptible to malaria. Moreover, Nse, Nsi and Nsr represented those exposed,&#13;
infected and recovered from malaria, respectively, but susceptible to Lassa fever.&#13;
The rodent population (obtained from the literature) were classi ed as Sd, Ed and&#13;
Id represented those susceptible, exposed and infected, respectively. Furthermore,&#13;
the mosquito population were classi ed as Sm, Em and Im denoted those suscepti ble, exposed and infected, respectively. Using next generation matrix method, the&#13;
basic reproduction number, R0(a, t) of the co-infection model was computed, where&#13;
a is the age and t is the time. Applying perturbation method, stable subharmonic&#13;
bifurcation solutions were determined. With the aid of suitable Lyapunov function,&#13;
the stability of the equilibra were explored. Using Pontryagin maximum principle,&#13;
v&#13;
necessary condition for the optimal control were derived. Numerical analyses of the&#13;
model were carried out to investigate the parameters most responsible for disease&#13;
transmission using data obtained from World Health Organization database.&#13;
The established mathematical model gives a system of fourteen ordinary dif ferential equations with the  rst four as:&#13;
N&#13;
0&#13;
rs(t) = αγvNes + ηvσNis − µNrs + bw1φ0Nes,&#13;
S&#13;
0&#13;
m(t) = ξm − λmSm(t)Nis − µmSm,&#13;
E&#13;
0&#13;
m(t) = λmSm(t)Nis − µmEm,&#13;
I&#13;
0&#13;
m(t) = −µmIm,&#13;
where, µ and µm are human and mosquito death rate respectively;λm is the trans mission rate. The R0(a, t) was computed as R0(a, t) = √&#13;
RhmRmm, where Rmm and&#13;
Rhm are the vector and human threshold parameters, respectively. An in nite num ber (n) of stable subharmonic solutions was obtained as Nss(t, ai) = Nssn&#13;
(t + α),&#13;
where α is the treatment rate of infected human with malaria. The disease-free&#13;
and endemic equilibria were found to be globally and asymptotically stable since&#13;
R0(a, t) = 0.496 and R0(a, t) = 2.684, respectively. Conditions for existence and&#13;
uniqueness of optimality system, u1u2u3 were established, were u1u2u3 are the con trol functions. Vectors biting rate b and contact rate w1 among eleven positive&#13;
sensitivity index parameter values: µ = 0.000038, b = 1.58, w1 = 1.38,µm = 0.054,&#13;
γv = 0.00027, σ = 0.012, α = 0.50, ηv0.0053, λm = 0.26, ξm = 0.50 and φ0 = 0.24&#13;
contributed majorly to the transmission of the diseases.&#13;
The formulated model captured seasonal variation of vectors and also showed&#13;
that co-infection of malaria and Lassa fever increased mortality rate in infected&#13;
patients.
</description>
<dc:date>2021-02-01T00:00:00Z</dc:date>
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