A Deterministic Modeling Approach in Identifying the Optimal Screening for Human Immunodeficiency Virus Management

The enormous success made in the development of drugs for Human Immunodeficiency Virus (HIV) infection to suppress the viral load of the disease, avert death and suffering due to the disease, no HIV individual is supposed to experience morbidity or death due to the disease. However, most affected people only avail themselves for HIV test at symptomatic stage which leads to morbidity and mortality due to Acquired Immune Deficiency Syndrome (AIDS). In the absence of HIV vaccine for the prevention against HIV infection, HIV screening test would be the next close to vaccination. This aim of this study was to developed a model that determine the optimal HIV screening sequence as intervention for HIV in a population. The concept of screening was brought into system of non-linear differential equations to obtain the deterministic model. The screening sequence and the varying population proportions were used in determining the optimal screening. The findings were that; when the systematic HIV screening of the population was done in six years, mortality and morbidity occurrences were reduced, and subsequent systematic screening reduced morbidity and mortality more in the population; and screening thirty percent of the population every year saved the lives of ninety percent HIV individuals and forestalled ninety percent of them from experiencing morbidity. It was noted also that screening fifty percent of the population three times within six years produced the same effect.

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Publication Details

Journal
Mikailalsys Journal of Mathematics and Statistics
Published
2025-03-14
DOI
https://doi.org/10.58578/mjms.v3i2.5146
Primary Topic
Hepatitis C virus research
Type
article
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article

A Deterministic Modeling Approach in Identifying the Optimal Screening for Human Immunodeficiency Virus Management

Adeniyi Oyewole Ogunmola, Emmanuel Teju Jolayemi
Mikailalsys Journal of Mathematics and Statistics
Hepatitis C virus research
article

A Deterministic Modeling Approach in Identifying the Optimal Screening for Human Immunodeficiency Virus Management

Adeniyi Oyewole Ogunmola, Emmanuel Teju Jolayemi
article en

Abstract

The enormous success made in the development of drugs for Human Immunodeficiency Virus (HIV) infection to suppress the viral load of the disease, avert death and suffering due to the disease, no HIV individual is supposed to experience morbidity or death due to the disease. However, most affected people only avail themselves for HIV test at symptomatic stage which leads to morbidity and mortality due to Acquired Immune Deficiency Syndrome (AIDS). In the absence of HIV vaccine for the prevention against HIV infection, HIV screening test would be the next close to vaccination. This aim of this study was to developed a model that determine the optimal HIV screening sequence as intervention for HIV in a population. The concept of screening was brought into system of non-linear differential equations to obtain the deterministic model. The screening sequence and the varying population proportions were used in determining the optimal screening. The findings were that; when the systematic HIV screening of the population was done in six years, mortality and morbidity occurrences were reduced, and subsequent systematic screening reduced morbidity and mortality more in the population; and screening thirty percent of the population every year saved the lives of ninety percent HIV individuals and forestalled ninety percent of them from experiencing morbidity. It was noted also that screening fifty percent of the population three times within six years produced the same effect.

Mikailalsys Journal of Mathematics and StatisticsVol. 3(2)
Kwararafa University (NG), Federal University Wukari
Openalex Percentile: Top 93%
Hepatitis C virus research
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A Deterministic Modeling Approach in Identifying the Optimal Screening for Human Immunodeficiency Virus Management — Adeniyi Oyewole Ogunmola, Emmanuel Teju Jolayemi · Mikailalsys Journal of Mathematics and Statistics (2025) | TGRS Research Map | TGRS