Proceedings of International Conference on Applied Innovation in IT  ·  2026/06/12  ·  Vol. 14  ·  Issue 4  ·  pp. 353–359
Regression-Based Sensitivity Characterization of the SIR Model
Banen Kadhim Jawad and Ali Hussein Shuaa Al-Taie
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Abstract
This work presents a statistical regression-based sensitivity analysis to extract interpretable analytical relationships between epidemiological parameters. Unlike previous studies that relied on numerical simulation of the SIR model, this provides a streamlined predictive framework for decision making and rapid evaluation. Mathematical models help us to study the transmission of infectious diseases and evaluate the effectiveness of public health strategies. Here, we emphasize the sensitivity of the Susceptible–Infected–Recovered (SIR) model. A linear regression equation is introduced to facilitate an analysis of the transmission of COVID-19 in Iraq. The model is modified with 2020 data provided by the Iraqi Ministry of Health, which corresponds to the first wave of the pandemic. The estimated value of the basic reproduction number (R_0) is about 2.5 which explains the fast exponential rise in infection numbers at this period. There is an inverse linear connection between the rate of recovery (γ) and the duration of the epidemic (t ̂_d) given by t ̂_d = -432.87γ + 259.75. The findings show that improved recovery processes also reduce the epidemic period. Furthermore, increasing the transmission coefficient (β) results in an infection peak more quickly, as indicated by T ̂_peak = -163.36β + 93.54. These results are important for improving epidemiological modeling and helping decision makers make important decisions about future disease outbreaks.
Keywords
COVID-19 Iraq Regression Sensitivity Analysis SIR
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