Mathematical Exploration of Malaria Transmission Dynamics: Insights from Fractional Models and Numerical Simulation

Abstract This study presents an innovative mathematical model denoted as the fractional SIP(H)–SI(M) model, which aims to analyze and understand the dynamics of malaria transmission and spread. This model is distinguished by incorporating memory effects through fractional differential equations, allowing for a more accurate and realistic analysis of disease spread compared to traditional models. The proposed model is applied to Algeria by estimating its parameters using recent health data (from 2000). The results revealed that the disease‐free equilibrium is stable only when the basic reproduction number is less than one, indicating that controlling the spread of malaria and possibly eradicating it can be achieved by implementing appropriate preventive measures. Simulations also demonstrated a direct correlation between the rate of infection transmission and an increase in the number of infected individuals, highlighting the need for swift action when signs of an outbreak emerge. Based on these findings, a set of preventive measures is recommended, including insecticide spraying programs, widespread distribution of insecticide‐treated bed nets, and implementation of effective treatment protocols for infected individuals. This study also emphasizes the importance of continuous monitoring of health data and updating model parameters to ensure the effectiveness and sustainability of preventive measures.

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Publication Details

Journal
Advanced Theory and Simulations
Published
2024-11-06
DOI
https://doi.org/10.1002/adts.202400630
Citations
4
Primary Topic
Mathematical and Theoretical Epidemiology and Ecology Models
Type
article
Field-Weighted Citation Impact
2.17
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article

Mathematical Exploration of Malaria Transmission Dynamics: Insights from Fractional Models and Numerical Simulation

Noureddine Benhamidouche, Bilal Basti, Souad Bounouiga
4 citations
Advanced Theory and Simulations
Mathematical and Theoretical Epidemiology and Ecology Models
2.17
article

Mathematical Exploration of Malaria Transmission Dynamics: Insights from Fractional Models and Numerical Simulation

Noureddine Benhamidouche, Bilal Basti, Souad Bounouiga
article en
4 citations

Abstract

Abstract This study presents an innovative mathematical model denoted as the fractional SIP(H)–SI(M) model, which aims to analyze and understand the dynamics of malaria transmission and spread. This model is distinguished by incorporating memory effects through fractional differential equations, allowing for a more accurate and realistic analysis of disease spread compared to traditional models. The proposed model is applied to Algeria by estimating its parameters using recent health data (from 2000). The results revealed that the disease‐free equilibrium is stable only when the basic reproduction number is less than one, indicating that controlling the spread of malaria and possibly eradicating it can be achieved by implementing appropriate preventive measures. Simulations also demonstrated a direct correlation between the rate of infection transmission and an increase in the number of infected individuals, highlighting the need for swift action when signs of an outbreak emerge. Based on these findings, a set of preventive measures is recommended, including insecticide spraying programs, widespread distribution of insecticide‐treated bed nets, and implementation of effective treatment protocols for infected individuals. This study also emphasizes the importance of continuous monitoring of health data and updating model parameters to ensure the effectiveness and sustainability of preventive measures.

Advanced Theory and SimulationsVol. 8(2)
University Mohamed Boudiaf of M'sila (DZ)
Openalex Percentile: Top 12%
Mathematical and Theoretical Epidemiology and Ecology Models
2.17
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Mathematical Exploration of Malaria Transmission Dynamics: Insights from Fractional Models and Numerical Simulation — Noureddine Benhamidouche, Bilal Basti, et al. · Advanced Theory and Simulations (2024) | TGRS Research Map | TGRS