The James-Stein shrinkage estimation though biased, improves parameter estimation by reducing the mean squared error. If well constructed, this provides an environment that it can be used to improve model fitting in situations where the distribution of the data may achieve normality under certain conditions. This study introduces the James-Stein estimation technique to improve parameter estimation for Bimodal exponential regression model fitting for analyzing epidemiological rates like Dengue fever cases done by de Costa. We incorporate the James-Stein shrinkage in parameter estimation on the Multivariate Log-Normal distribution as it provides the normality conditions required by this technique. The model’s parameters are estimated using the shrunk maximum likelihood estimation, and simulations are performed to validate their accuracy and reliability. The James-stein induced model show some improvement in precision, accuracy and stability in fitting epidemiological data compared to the traditional maximum likelihood estimation. Furthermore, different properties and measures to evaluate the model’s goodness-of-fit are analyzed. The results from all analyzed measures show an improvement in model fitting when we incorporate the James-Stein shrinkage technique. Apart from Dengue fever data for the federal district of Brazil, for diverse in applicability, we consider seasonal Cholera rates data for Zambia from 2021 to 2026. As real time example, we analyze a regression model fit to the weekly rates of seasonal Cholera rates for three districts namely Chilanga, Mpulungu and Lusaka. We extend the application to Ebola rates outbreaks for 2026 in DR Congo and Uganda.
*Keywords:* Epidemiological Rates, James-Stein Estimation, Maximum likelihood, Multivariate Log-Normal, Mean Squared Error, Regression Model
