ABM is a powerful tool in this context because of its ability to fuse a number of important features which influence the spread of disease. In particular, it can bring together aspects of disease which elude other methodologies:
- ABM can incorporate spatiality in a way that, for example, SIR models can't, capturing the patterns that John Snow highlighted in his early work in GIS
- because ABM can simulate individuals with heterogeneous ages, healths, access to resources, and so forth, it allows for richer representation of disease transmission (i.e. the unusual tendency of Swine Flu to infect the young more than the elderly). System dynamics models of any kind have difficulty doing the same
- human behaviours such as seeking treatment or moving around the environment rather than staying home obviously influence the spread of disease; these dynamic processes can't be addressed by standard GIS techniques
- all of these apply not only to the susceptible individuals, but to the disease and its vectors as well: simulating how tsetse flies move relative to a river or how long a reservoir of bacteria will live on a water pump allows us to project the interactions among these things
Putting my money where my mouth is, Ivan and I have been developing a simulation - or rather, a library to support simulations - which can simply and quickly generate patterns of the spread of disease. The model Ivan presented at MSF Scientific Day highlighted how human-to-human infections such as influenza can spread through an environment, in contrast with water-borne or insect-borne diseases.
A sample visualisation of human-to-human infections is shown in the following video, which reflects a very simple model of individuals moving around the environment throughout the incubation period of the disease, only choosing to seek treatment or stay home during the second phase of the disease. Individuals have homes, and may choose to visit random "friends" based on how distant they are: they therefore frequently make short trips and occasionally travel much longer distances.
This is a very simple case, with very simple behaviours and transmission mechanisms; regardless, it tells the story of how individuals bring diseases home from the larger city to their small towns. It suggests how an epidemic might develop in space and time, and it represents a first step toward understanding how we can respond to and limit the outbreak of disease.
I'm very interested in this project, and while I don't have much time to devote to it, I hope to be writing about it here in the future. In the meantime, I have some (shamefully underdocumented) code available on GitHub here, so do watch both these spaces for updates.