Scholarlink

<p>This manuscript is devoted to formulating an infectious disease model based on the concept of fractional calculus. Recently the infectious disease due to coronavirus has remained a big threat all over the world. Because the mentioned pandemic has produced great human loss all over the globe. Researchers have worked very well to investigate the transmission of such diseases using different perspectives. One important tool to deal with such a problem is related to mathematical models. Therefore, a mathematical model for infectious diseases like COVID-19 is formulated, incorporating strategies and the impact of social media platforms such as <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40808_2025_2631_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(\mathbb {X}\)</EquationSource> </InlineEquation> (formerly Twitter). For this study, we use the Liouville-Caputo (LC) fractional-order derivative. The existence theory, stability analysis, and numerical simulations are investigated. Fixed point theory is applied to establish the existence results, and numerical tools are utilized for graphical simulations of the results. The reproduction number is calculated using the next-generation matrix, and the sensitivity of each parameter involved in the reproduction number is analyzed. The global asymptotic stability of the disease-free and endemic equilibrium points is established. The numerical results are obtained using the fractional RK4 method. Notably, the dynamics of the susceptible, exposed, asymptomatic, and infected populations demonstrate that higher fractional orders result in faster transitions between compartments, highlighting the critical influence of positive and negative information spread on <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40808_2025_2631_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(\mathbb {X}\)</EquationSource> </InlineEquation> in shaping public behavior and disease control outcomes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mathematical model and simulations of fractional order for an infectious disease involving social media

  • 期刊论文

  • 发表日期：

    2025年9月26日

查看全文
  • Kamal Shah,
  • Thabet Abdeljawad,
  • Zeeshan Ali

摘要

This manuscript is devoted to formulating an infectious disease model based on the concept of fractional calculus. Recently the infectious disease due to coronavirus has remained a big threat all over the world. Because the mentioned pandemic has produced great human loss all over the globe. Researchers have worked very well to investigate the transmission of such diseases using different perspectives. One important tool to deal with such a problem is related to mathematical models. Therefore, a mathematical model for infectious diseases like COVID-19 is formulated, incorporating strategies and the impact of social media platforms such as \(\mathbb {X}\) (formerly Twitter). For this study, we use the Liouville-Caputo (LC) fractional-order derivative. The existence theory, stability analysis, and numerical simulations are investigated. Fixed point theory is applied to establish the existence results, and numerical tools are utilized for graphical simulations of the results. The reproduction number is calculated using the next-generation matrix, and the sensitivity of each parameter involved in the reproduction number is analyzed. The global asymptotic stability of the disease-free and endemic equilibrium points is established. The numerical results are obtained using the fractional RK4 method. Notably, the dynamics of the susceptible, exposed, asymptomatic, and infected populations demonstrate that higher fractional orders result in faster transitions between compartments, highlighting the critical influence of positive and negative information spread on \(\mathbb {X}\) in shaping public behavior and disease control outcomes.