Welcome to our epidemic simulator created by Jack Liu and Victor Pham for HooHacks 2020. Inspired by the recent global crisis around Covid-19, we wanted to create a tool to help people understand how epidemics spread and what factors influence the final outcome. This simulation runs based on the SIR epidemic model where the population is categorized as either susceptible, infected, or removed. We modified this slightly to account for infected individuals who display no symptoms (carriers) as well as those who have unfortunately succumbed to the disease.
The simulation involves 6 communities with individuals represented as dots and the quantity of each type of individual graphed over time. In the beginning, there is a single infected individual and each person is moving randomly. For each successive frame of the simulation, there are 5 algorithms that are performed to determine the next state:
This loop continues until a certain critical number of infected individuals is detected (does not include carriers). Once these thresholds are met, several changes to the previous algorithms are implemented.
The power of this simulation is that almost every parameter of the simulation is variable. This means that you can use this simulation to investigate when is the best time to implement travel restrictions, or what impacts a few individuals that do not participate in social distancing has on the overall outcome. Try to experiment and see what factors have the greatest impact on flattening the curve. The sliders control the values of each parameter and using the space bar pauses the simulation while the "r" key resets it from the beginning.