An SIA model of HIV transmission in Ghana

Abstract

We seek to discuss how the transmission of HIV infection, and hence AIDS disease, depends on various biological and social factors, which may be different within and between the different population groups. In this paper an SIA compartment model of the transmission dynamics of HIV/AIDS is developed using Ghana Data. The resulting system of three non-linear differential equations was analyzed in respect of stability of the three equilibrium points namely the disease free which was found to be locally asymptotically stable and two endemic equilibrium points which were found to be 96 Stephen Eduafo et al. stable. Further analysis to determine the conditions for the breakout of epidemic were done using the basic reproductive number of the infection. It was found that the rate of transition from HIV infected to AIDS relative to the rate of transition from susceptible to HIV infected state would need to be increased in order to effectively control the spread of the disease.

Analisis Kestabilan Titik Tetap Pada Model Matematika Penyebaran Hiv/Aids

“…[1] melakukan penelitian analisa kestabilan penyebaran HIV/AIDS dengan edukasi kesehatan melalui perluasan model SI (Susceptible-Infected), dampak edukasi kesehatan pada penyebaran penyakit HIV/AIDS dikaji dengan analisis sensitivitas angka reproduksi efektif terhadap semua parameter yang mendorong dinamika penyakit. [2] membahas tentang model SIA transmisi HIV di Ghana. Berdasarkan referensi dari penelitian yang telah dilakukan sebelumnya, dalam penelitian ini dikaji model matematika dinamika transmisi atau penularan HIV/AIDS.…”

Modeling Hiv/Aids Using Shat Model

“…Furthermore, Susceptible (S) is a susceptible individual, who is healthy and can contract the disease; Infected (I) is an infected individual, and, Recovered (R) is a recovered individual (R) who is immune to the disease [4]- [6]. The development of the SIR Epidemic Model in the modeling of HIV/AIDS resulted in the Susceptible-Infected-AIDS (SIA) Epidemic Model [7], [8]. The epidemic model continues to develop and produces several models, including the SEIR model [9], SJAT [10], SEIA [11], SICA [12].…”

Current forecast of HIV/AIDS using Bayesian inference

“…Additionally, inspired in a full data-driven approach, we have tried to use all the reliable data available to forecast the spread of the HIV/AIDS disease, keeping in mind that a simple model may fit better than a complex one (Roda et al, 2020). Thus, we formulate a single-stage SIA-type mathematical model similar to the proposed in Arazoza and Lounes (2002), Biswas and Pal (2017), Cai et al (2008), Eduafo et al (2015). In our model, the total population N , is divided into the following three epidemiological classes: susceptible individuals S, infective individuals who are the infected and infectious individuals that have not yet developed AIDS symptoms I , and AIDS patients who are infected and with AIDS symptoms A.…”