UCLA

Mathematical models used in epidemiology, such as compartmental models, have the power to reveal the risk of infectious disease outbreaks and can impact the course of action for public health interventions. Despite these models being inexact at capturing the complexities of real-world disease transmission, they are capable of providing meaningful insight into physical systems and can ultimately support decision-making. These models use input parameters that represent characteristics of the physical system of interest. However, the true values of these parameters are often unknown and cannot be directly measured. Hence, calibration, or the process of identifying the optimal values of these parameters to best fit the observed data, is utilized to improve predictions and reliability of the model. We employ a dynamic compartmental model, known as an SVEIR model, to study the transmission of measles. Although declared an eradicated disease in the United States in 2000, there has been recent attention on several measles outbreaks throughout the country, most notably an outbreak beginning in Gaines County, Texas at the start of 2025. We perform four different statistical calibration methods on the SVEIR model using daily incidence data from the West Texas outbreak. We report estimated parameter values and basic reproduction numbers for the model calibrated using different methods. We give a comparison of predictive root mean square errors of all calibration methods and find that the optimal prediction calibration method returned the lowest predictive error. This study highlights the differences in calibration methods, hopes to provide an improved understanding of the current state of the ongoing measles outbreak in West Texas, and underscores avenues for future analysis.