Parameter variance is used to measure the dispersion of data or the deviation of individual data points from the mean. A high variance indicates that the data are widely spread around the mean, reflecting greater variability within the dataset. This study introduces four novel methods for constructing confidence intervals for the variance of the Zero-Inflated two-Parameter Rayleigh distribution. These include the percentile bootstrap, the bootstrap method with standard error, the standard method based on a large sample, and generalized confidence interval approaches. A simulation-based comparison was conducted using coverage probability and expected length as performance criteria. The findings indicate that the generalized confidence interval and standard method achieved coverage probabilities closest to the nominal confidence level. Among these methods, the generalized confidence interval demonstrated the highest efficiency. Additionally, the proposed methods were applied to real-world data on COVID-19 mortality rates in Malaysia during September 2021.