The use of public health records to develop mathematical models for epidemic decision-making began with Bernoulli in the 18th century. In the 19th century, Florence Nightingale used statistics and data visualization to reduce mortality in military hospitals, laying the foundations of modern decision-making in public health. These efforts were followed in the 20th century by major contributions from Ross and McDonald, and others, who advanced the mathematical modeling of infectious diseases. These foundational studies helped establish mathematical epidemiology as an independent discipline, leading to the development of influential models such as the Kermack-McKendrick and Reed-Frost models, which remain central to theoretical research. Over time, numerous advancements have emerged in this field. Despite the challenges posed by uncertainty and incomplete epidemic records—resulting from heterogeneity in susceptibility and human behavior—there has been a growing trend to integrate data into mathematical models for epidemiological decision-making. This resurgence, reminiscent of early modeling efforts, has been driven by advances in mathematical techniques and computational power. This section explores contemporary models spanning different scales, from within-host to between-host dynamics, encompassing both deterministic and stochastic approaches.