<p>In this study, we develop a stochastic model that captures the dynamics of HIV infection, encompassing susceptible individuals, asymptomatic HIV-positive individuals, and those exhibiting symptoms. Initially, we examine the existence and stability of both disease-free and endemic equilibria within the deterministic version of the model. Our analytical findings indicate that the basic reproduction number, <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3908_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="22" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </math></EquationSource> <EquationSource Format="TEX">$\mathcal{R}_{0}$</EquationSource> </InlineEquation>, is a pivotal factor in determining the uniqueness and global stability of these equilibria. Furthermore, we explore the impact of environmental noise on the HIV disease model, identifying two critical thresholds, <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3908_Article_IEq2.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msubsup> <mi mathvariant="script">R</mi> <mn>1</mn> <mi>s</mi> </msubsup> </math></EquationSource> <EquationSource Format="TEX">$\mathcal{R}_{1}^{s}$</EquationSource> </InlineEquation> and <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3908_Article_IEq3.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msubsup> <mi mathvariant="script">R</mi> <mn>2</mn> <mi>s</mi> </msubsup> </math></EquationSource> <EquationSource Format="TEX">$\mathcal{R}_{2}^{s}$</EquationSource> </InlineEquation> (with <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3908_Article_IEq4.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="67" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msubsup> <mi mathvariant="script">R</mi> <mn>2</mn> <mi>s</mi> </msubsup> <mo>&lt;</mo> <msubsup> <mi mathvariant="script">R</mi> <mn>1</mn> <mi>s</mi> </msubsup> </math></EquationSource> <EquationSource Format="TEX">$\mathcal{R}_{2}^{s} &lt; \mathcal{R}_{1}^{s}$</EquationSource> </InlineEquation>). If <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3908_Article_IEq5.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msubsup> <mi mathvariant="script">R</mi> <mn>1</mn> <mi>s</mi> </msubsup> </math></EquationSource> <EquationSource Format="TEX">$\mathcal{R}_{1}^{s}$</EquationSource> </InlineEquation> is less than unity, the disease is likely to be eradicated; conversely, if <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3908_Article_IEq6.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msubsup> <mi mathvariant="script">R</mi> <mn>2</mn> <mi>s</mi> </msubsup> </math></EquationSource> <EquationSource Format="TEX">$\mathcal{R}_{2}^{s}$</EquationSource> </InlineEquation> exceeds unity, the disease will persist, and a unique stationary distribution will emerge. Additionally, our numerical simulations reveal that when <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3908_Article_IEq7.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="97" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msubsup> <mi mathvariant="script">R</mi> <mn>2</mn> <mi>s</mi> </msubsup> <mo>&lt;</mo> <mn>1</mn> <mo>&lt;</mo> <msubsup> <mi mathvariant="script">R</mi> <mn>1</mn> <mi>s</mi> </msubsup> </math></EquationSource> <EquationSource Format="TEX">$\mathcal{R}_{2}^{s} &lt; 1 &lt; \mathcal{R}_{1}^{s}$</EquationSource> </InlineEquation>, the disease may still face extinction. From an epidemiological viewpoint, our observations suggest that a decrease in environmental noise intensity results in a reduction of the oscillation amplitude in the disease dynamics. Conversely, an increase in noise intensity is associated with a lower mean of infectious individuals and a left-skewed distribution.</p>

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Stochastic analysis of an HIV model with various infection stages

  • Feng Rao,
  • Yiping Tan,
  • Xinze Lian

摘要

In this study, we develop a stochastic model that captures the dynamics of HIV infection, encompassing susceptible individuals, asymptomatic HIV-positive individuals, and those exhibiting symptoms. Initially, we examine the existence and stability of both disease-free and endemic equilibria within the deterministic version of the model. Our analytical findings indicate that the basic reproduction number, R 0 $\mathcal{R}_{0}$ , is a pivotal factor in determining the uniqueness and global stability of these equilibria. Furthermore, we explore the impact of environmental noise on the HIV disease model, identifying two critical thresholds, R 1 s $\mathcal{R}_{1}^{s}$ and R 2 s $\mathcal{R}_{2}^{s}$ (with R 2 s < R 1 s $\mathcal{R}_{2}^{s} < \mathcal{R}_{1}^{s}$ ). If R 1 s $\mathcal{R}_{1}^{s}$ is less than unity, the disease is likely to be eradicated; conversely, if R 2 s $\mathcal{R}_{2}^{s}$ exceeds unity, the disease will persist, and a unique stationary distribution will emerge. Additionally, our numerical simulations reveal that when R 2 s < 1 < R 1 s $\mathcal{R}_{2}^{s} < 1 < \mathcal{R}_{1}^{s}$ , the disease may still face extinction. From an epidemiological viewpoint, our observations suggest that a decrease in environmental noise intensity results in a reduction of the oscillation amplitude in the disease dynamics. Conversely, an increase in noise intensity is associated with a lower mean of infectious individuals and a left-skewed distribution.