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<p><?tk 2?>Coronavirus disease (COVID-19), caused by a novel coronavirus, has emerged as one of the most severe global pandemics. In India, a large population and limited healthcare resources, coupled with inadequate testing, exacerbated early transmission. This study develops a compartmental mathematical model by categorizing the infectious population into asymptomatic/undetected and symptomatic/detected compartments. The model is calibrated with Indian data to estimate transmission and recovery rates, and the basic reproduction number (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R_0\)</EquationSource> </InlineEquation>) is derived. Stability and sensitivity analyses identify key parameters influencing disease dynamics. An optimal control framework integrating preventive measures, treatment, and hospitalization is implemented, and the cost-effectiveness of interventions is evaluated using ICER, ACER, and IAR metrics. Results indicate that combined interventions were most effective in the first wave, whereas treatment-focused strategies were more economically efficient during the second wave. These findings highlight the value of combining mathematical modeling with economic assessment to inform adaptive, cost-effective COVID-19 control strategies.</p>

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Mathematical modeling, optimal control, and cost-effectiveness of multi-wave COVID-19 interventions: a case study of India

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • A. Karthik,
  • Akhil Kumar Srivastav,
  • Mini Ghosh

摘要

Coronavirus disease (COVID-19), caused by a novel coronavirus, has emerged as one of the most severe global pandemics. In India, a large population and limited healthcare resources, coupled with inadequate testing, exacerbated early transmission. This study develops a compartmental mathematical model by categorizing the infectious population into asymptomatic/undetected and symptomatic/detected compartments. The model is calibrated with Indian data to estimate transmission and recovery rates, and the basic reproduction number ( \(R_0\) ) is derived. Stability and sensitivity analyses identify key parameters influencing disease dynamics. An optimal control framework integrating preventive measures, treatment, and hospitalization is implemented, and the cost-effectiveness of interventions is evaluated using ICER, ACER, and IAR metrics. Results indicate that combined interventions were most effective in the first wave, whereas treatment-focused strategies were more economically efficient during the second wave. These findings highlight the value of combining mathematical modeling with economic assessment to inform adaptive, cost-effective COVID-19 control strategies.