title: Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities.
abstract: BACKGROUND
  This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions.
  
  
  METHODS
  As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe.
  
  
  RESULTS
  An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser.
  
  
  CONCLUSIONS
  This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it.
authors.affiliation: Internet Innovation Centre, Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Canada. macbork@microsoft.com
authors.forename: Maciej
authors.initials: M
authors.lastname: Borkowski
authors.forename: Blake W
authors.initials: BW
authors.lastname: Podaima
authors.forename: Robert D
authors.initials: RD
authors.lastname: McLeod
mesh_terms: D003198:Computer Simulation; D057225:Data Mining; D004196:Disease Outbreaks; D018562:Disease Transmission, Infectious; D004812:Epidemiologic Methods; D015658:HIV Infections / Q000635:transmission; D006801:Humans; D008962:Models, Theoretical; D014195:Travel