Today, infectious diseases represent a threatening concern for human health. Understanding their transmission, and possibly forecasting the dynamics of these pathogens, represents both a scientific and sanitary emergency. To this goal, mathematical modeling has been a widely used tool. Nevertheless, they have important limitations to explicitly model the mechanisms involved in the infectious processes at the individual level. Thanks to the increase of computing capacity, computational models such as individual-based models (IBMs) are very relevant for understanding the complexity of mechanisms at the individual level that can be involved in disease outbreaks. Their computational formalism allows a large flexibility, while they rely on the same philosophy than current models in mathematical epidemiology that have proved their relevance. In this chapter, we review the main qualities of IBMs, what kind of new knowledge they can bring and they have already produced in epidemiological modeling. Then, we highlight their caveats and what could be developed during the future years to make IBMs a more reliable and useful approach.