Algorithm complexity refers to the computational resources required by an algorithm to solve a problem of a certain size. These resources can include time (how long it takes to run) and space (the amount of memory it uses). In epidemiology, understanding algorithm complexity is crucial for processing large datasets and running simulations efficiently.