A Logistic Regression Model to Predict Risk Factors Associated with HIV Prevalence: A Comprehensive Review

Danung Friday Ezekiel, Kazeem E. Lasisi, Muhammad Sanda Adamu, Abuzairu Ahmad, Temen Nanfwang Yunana

Abstract


The Human Immunodeficiency Virus (HIV) is a virus that attacks immune cells known as CD4 cells, which are a type of T cell. These are white blood cells that circulate throughout the body, detecting defects and abnormalities in cells as well as infections. When HIV targets and infiltrates these cells, it reduces the body's ability to fight off other diseases. This raise the likelihood and severity of opportunistic infections and cancer. Since there has been no effective cure for the global HIV/AIDS epidemic for more than 40 years, current control strategies have centered on reducing (or eliminating) new infections through risk reduction. As a result, the modified logistic regression model is used to predict HIV risk factors. The model will be used to assess the strength of the association between each of the hypothesized risk factors, as well as to determine the efficiency of the modified model, using the statistical tool SPSS version 25.0


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