Modeling COVID-19 Wave Dynamics in India Using the SIR Model: A Data-Driven Epidemiological Analysis of First and Second Waves (2020–2021)

The COVID-19 pandemic has posed unprecedented challenges to global public health systems, with India experiencing multiple waves characterized by distinct transmission dynamics and healthcare burdens. This study presents a comprehensive data-driven and epidemiological analysis of COVID-19 progression in India, with a particular focus on the comparative assessment of the first and second waves during the period 2020–2021. Utilizing publicly available daily time-series data on confirmed, recovered, and deceased cases, we examine temporal trends and quantify variations in infection spread and recovery patterns. To provide a theoretical and analytical framework, the classical Susceptible–Infected–Recovered (SIR) model is employed to simulate the transmission dynamics of the disease. Model parameters, including the transmission rate (β) and recovery rate (γ), are estimated from real-world data, enabling an evaluation of infection growth and decline across different phases of the pandemic. The integration of empirical data with the SIR framework allows for a deeper understanding of the underlying mechanisms governing wave formation and peak intensity. The results reveal that the second wave of COVID-19 in India exhibited significantly higher transmission intensity, with daily confirmed cases surpassing 400,000 at its peak, compared to approximately 90,000 during the first wave. Additionally, the lag between infection and recovery rates during the second wave indicates increased strain on healthcare infrastructure. Active case analysis further highlights the severity of the second wave, reaching nearly 3.7 million active cases, substantially exceeding the first wave peak. This study underscores the critical role of data-driven epidemiological modeling in capturing the dynamic behavior of infectious diseases. The findings provide valuable insights into the temporal evolution and severity of pandemic waves, which can inform public health strategies, resource allocation, and future outbreak preparedness. The proposed approach demonstrates the effectiveness of combining real-time data analysis with mathematical modeling to enhance the understanding of large-scale epidemic events.