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Chronic livestock diseases cause large financial loss and affect the animal health and welfare. Controlling these diseases mostly requires precise information on both individual animal and population dynamics to inform farmer’s decision. Mathematical models provide opportunities to test different control and elimination options rather implementing them in real herds, but these models require valid parameter estimation and validation. Fitting these models to data is a difficult task due to heterogeneities in livestock processes. In this paper, we develop an infectious disease modeling framework for a livestock disease (paratuberculosis) that is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Infection with MAP leads to reduced milk production, pregnancy rates, and slaughter value and increased culling rates in cattle and causes significant economic losses to the dairy industry in the US. These economic effects are particularly important motivations in the control and elimination of MAP. In this framework, an individual-based model (IBM) of a dairy herd was built and a MAP infection was integrated on top of it. Once the model produced realistic dynamics of MAP infection, we implemented an evaluation method by fitting it to data from three dairy herds from the Northeast region of the US. The model fitting exercises used least-squares and parameter space searching methods to obtain the best-fitted values of selected parameters. The best set of parameters were used to model the effect of interventions. The results show that the presented model can complement real herd statistics where the intervention strategies suggested a reduction in MAP but no elimination was observed. Overall, this research not only provides a complete model for MAP infection dynamics in a cattle herd, but also offers a method for estimating parameter by fitting IBM models.