Background The spread of pathogens poses a significant threat to public health, but strategies like herd immunity and social distancing can mitigate this risk. Despite the well-documented benefits of these strategies, their effectiveness hinges on widespread public adherence and understanding. Recent events, such as the SARS-CoV-2 pandemic, have underscored the challenges posed by public resistance and misinformation. Studies have highlighted increased skepticism and misinformation regarding vaccination, undermining herd immunity efforts. To address this growing need for resources to counter doubts about public health decisions, we sought to develop software using cellular automaton models that can not only educate individuals on the value of practices like social distancing and vaccinations on disease spread, but also showcase realistic results to improve understanding. Methods We developed a simulation tool using a discrete cellular automaton process, modeling disease spread through a population grid where individuals can change states (susceptible, infected, vaccinated). Users can input parameters such as disease transmissibility, vaccination rates, and social distancing protocols to simulate scenarios across time. This model was tested as an educational supplement in an immunology class. Additionally, we developed a probabilistic multi-state cellular automaton model, incorporating stochastic elements to reflect randomness in disease transmission and individual behaviors. We applied the model to real-world data from COVID-19 databases to evaluate the effectiveness of herd immunity and social distancing in different communities. Results A survey of students using the discrete simulation found 83% reporting the software was intuitive, while 100% felt it increased understanding of herd immunity. The probabilistic cellular automaton model illustrated the positive impact of vaccination and social distancing on herd immunity, demonstrating vaccine effectiveness can be nearly 650 times more effective when social distancing is enforced. Additionally, the simulations revealed emerging patterns, such as regions without disease spread, possibly informing new metrics for evaluating public health responses. Conclusion Overall, this probabilistic simulation can serve as a valuable epidemiological tool to help characterize public health conditions. Furthermore, this simulation can be used as an educational tool for the public to better understand the mechanisms of herd immunity and the impact of their own behavior on disease spread. Future improvements to this model can include consideration of state transition adjustments, external system shocks, and additional considerations to initial conditions. The simulation tool's user-friendly interface and adaptable parameters make it feasible for widespread educational use, especially in unprecedented times. Its generalizability extends to various public health scenarios, promoting broader understanding of epidemiological strategies.