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<p>Mathematical modelling is widely used to analyse and predict complex real-world systems. In infectious disease epidemiology, it provides a framework for understanding transmission dynamics and supporting control strategies. This article discusses two commonly used models: the Serfling regression model for seasonal disease outbreaks and the SIR compartmental model for epidemic spread. The Serfling model is applied to Influenza-A data from China, while the SIR model is fitted to COVID-19 data from South Africa with a time-dependent transmission rate <i>β</i>(<i>t</i>). Real data illustrate the role of modelling in surveillance, prediction, and public-health planning.</p>

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Mathematical Modelling in Epidemiology

  • 期刊论文

  • 发表日期：

    2026年1月1日

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  • Ruchi Kaur,
  • Naman Taggar,
  • Vaishnavi Rajagopalan,
  • Jaspreet Kaur

摘要

Mathematical modelling is widely used to analyse and predict complex real-world systems. In infectious disease epidemiology, it provides a framework for understanding transmission dynamics and supporting control strategies. This article discusses two commonly used models: the Serfling regression model for seasonal disease outbreaks and the SIR compartmental model for epidemic spread. The Serfling model is applied to Influenza-A data from China, while the SIR model is fitted to COVID-19 data from South Africa with a time-dependent transmission rate β(t). Real data illustrate the role of modelling in surveillance, prediction, and public-health planning.