Humans vs. Zombies: Data-driven Modeling of Disease Spread

Abstract

Disease spread in close-knit communities depends heavily on the natural immunity of the individuals in the community as well as on the individuals’ interactions within the environment. This study uses data from a game of tag called Humans vs. Zombies, played on a small liberal arts campus, to examine how this “human element” can affect the spread of diseases in such communities. We fit five disease models to our data and find the best-fit parameters for each model. We conclude that an SIR model with multiple susceptibility classes and sleep cycles modifications provides the best fit, showing that human behavior is an essential element in disease modeling and that the diurnal routine of the individuals plays an important role in disease spread. Other college campuses could model similar games to observe how different cultures of people affect the results, which also doubles as a fun student engagement opportunity.