In today’s globally connected society, contagious diseases can spread rapidly through populations, necessitating effective strategies to control outbreaks. This project develops a digital model designed to simulate the spread of infectious diseases, leveraging network theory to mirror the complex web of social interactions that influence disease transmission dynamics. The model creates maps where each point represents an individual within a social network, with lines connecting them to represent social interactions. These connections are dynamic, with points changing colour to reflect a person’s health status—whether they are susceptible, exposed, infected, recovered, or deceased—offering a real-time visualization of the disease’s progression. Several critical factors drive the model’s behaviour, including the probability of infection, chances of recovery, mortality rates, and the structure of social connections. The interaction matrix is a key feature, dictating the likelihood of contact between individuals and, consequently, influencing the rate at which the disease spreads. Additionally, vaccination strategies are incorporated into the model, accounting for variables such as distribution speed, vaccine effectiveness, and potential reinfection. Lockdown measures are also included, representing the impact of social distancing on reducing transmission. The resulting maps provide a clear and accessible visual representation of how contagious diseases spread through a community. They offer valuable insights into the effectiveness of public health strategies, such as vaccinations and lockdowns, in curbing outbreaks. This project contributes to Sustainable Development Goal 3 (Good Health and Well-being) by enhancing our understanding of disease control, ultimately aiding in the creation of more resilient healthcare systems.