Page 17 - POSTER FYP MAC-OGOS 2025

cawangan
Kelantan
K242/17
NAME: SITI NUR AFIZAH BINTI AZMAN
NAME: SITI NUR AFIZAH BINTI AZMAN
NAME: SITI NUR AFIZAH BINTI AZMAN
SUPERVISOR: FARAHANIE BINTI FAUZIE
SUPERVISOR: FARAHANIE BINTI FAUZIE
SUPERVISOR: FARAHANIE BINTI FAUZIE
ABSTRACT
osquito
study
This study presents a mathematical model of the Sterile Insect Technique (SIT), formulated to simulate mosquito population dynamics
population
Sterile
m
model
Technique
This This study presents a mathematical model of the Sterile Insect Technique (SIT), formulated to simulate m osquito population dynamics
formulated
simulate
of
mathematical
to
(SIT),
presents
Insect
the
a
dynamics
a
Fehlberg
with
the
Excel
to
Zika
(RKF45),
analyze
Runge-Kutt
method
in
numerica
model
the
associated with the Zika virus. Using the Runge-Kutta Fehlberg method (RKF45), the model was numerically solved in Excel to analyze
Using
associated associated with the Zika virus. Using the Runge-Kutt a Fehlberg method (RKF45), the model was numerica lly solved in Excel to analyze
was
lly
solved
virus.
the
across
compartments.
DTM-based
and
used
changes
validate
population population changes across six compartments. A previously published DTM-based solution was used to benchmark trends and validate
was
published
population changes across six compartments. A previously published DTM-based solution was used to benchmark trends and validate
solution
to
six
benchmark
previously
A
trends
SIT
simulation
this
insights
using
theoretical
model
that
provides
study
and
can
variation,
RKF45 reliability. Through simulation and parameter variation, this theoretical study using SIT model provides insights that can assist
Through
parameter
reliability.
assist
RKF45 RKF45 reliability. Through simulation and parameter variation, this theoretical study using SIT model provides insights that can assist
biologists in designing future field experiments for mosquito control strategies.biologists in designing future field experiments for mosquito control strategies.
biologists in designing future field experiments for mosquito control strategies.
PROBLEM STATEMENT OBJECTIVE
The Sterile Insect Technology (SIT) has emerged as a promising METHOD
theoretical approach to help suppress mosquito populations Apply the RKF45 method to solve
associated with the spread of Zika virus (ZIKV). Through the SIT model;
mathematical modeling, SIT can guide biologists in evaluating Utilize the DTM-based solution for Start
population dynamics before conducting experimental field validation;
strategies. This study presents a theoretical framework using a Analyze the effects of varying
system of nonlinear differential equations to simulate mosquito mating parameters (β₁ and β₂) on Define SIT Model Equation
dynamics under SIT conditions. By analyzing numerical outputs, the model outcomes. (ODE system from literature)
the model supports biologists in designing and refining
experimental strategies for mosquito population suppression.
Specify the Initial Condition and
Parameter Values
IMPLEMENTATION
Solve the Same Problem using RKF45 with the
same Initial Condition via Microsoft Excel
(fixed h = 0.1 and from t = 0.1 to 1.0)
Use DTM-Based Equation from previous research
Variable description as a benchmark only
(Substitute t-value manually using excel)
DTM-based solution evaluation by substituting t-values into the
existing polynomial equation. Used as a benchmark to validate
RKF45 simulation results. Simulate RKF45 Output for Parameter Variations
(Change β₁ = {0.5,0.6,0.8,0.9} and β₂ =
{0.1,0.2,0.4,0.5})
Graph Result and Interpret Dynamic
RKF45 Butcher Tableau Formula Manual RKF45 calculation in Excel for A(t), showing all Across 6 Mosquito Compartments
,

intermediate slope values k1 to k6 and the updated value
Parameter Table and Initial Conditions
End
RESULTS & DISCUSSION
a) Mosquito Population Dynamics in SIT Model Simulated Using RKF45 : Compartment trends for A(t), FM(t), MM(t), FNM(t), FSM(t), and Ms(t) over the time interval t = 0.0
to t = 1.0 using fixed step size.
A steep decline over time indicates
transition of females into reproductive
compartments or natural death, typical in
early-stage SIT dynamics.
Increase from sterile male mating, do
not contribute to hatch rates.
Population increases with active mating
rates and egg production.
The aquatic population shows rapid initial
growth due to oviposition before stabilizing
near equilibrium, reflecting environmental
capacity limits in the SIT model.
Sterile male levels remain high due to release
strategies, maintaining suppression pressure
and influencing the proportions of sterile
offspring.
The wild male population remains relatively
stable, providing mating competition against
released sterile males—critical in the SIT mating
dynamic.

b) Sensitivity Simulation for Mating Parameters β₁ and β₂ : Showing how changes in mating rates B₁ and B₂ influence reproductive and non-reproductive female populations in
the SIT model (β₁: 0.5,0.6,0.7,0.8,0.9 and β₂: 0.1,0.2,0.3,0.4,0.5).

Higher β₁ raises reproductive females, while higher β₂ enhance sterile ones

CONCLUSION & RECOMMENDATIONCONCLUSION & RECOMMENDATION
CONCLUSION & RECOMMENDATION

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