Table of Contents
Epidemiology Research International
Volume 2012, Article ID 716072, 18 pages
http://dx.doi.org/10.1155/2012/716072
Research Article

Extracting Data from Disparate Sources for Agent-Based Disease Spread Models

1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 2N2
2Office of the Chief Engineer, Canadian Network for Public Health Intelligence, Winnipeg, MB, Canada R3E 3R2
3Technology Development, MTS Allstream, Winnipeg, MB, Canada R3C 3V6

Received 20 October 2011; Revised 8 February 2012; Accepted 5 March 2012

Academic Editor: Jacek A. Kopec

Copyright © 2012 M. Laskowski et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a review and evaluation of real data sources relative to their role and applicability in an agent-based model (ABM) simulating respiratory infection spread a large geographic area. The ABM is a spatial-temporal model inclusive of behavior and interaction patterns between individual agents. The agent behaviours in the model (movements and interactions) are fed by census/demographic data, integrated with real data from a telecommunication service provider (cellular records), traffic survey data, as well as person-person contact data obtained via a custom 3G smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion and the role of data in calibrating and validating ABMs. The data become real-world inputs into susceptible-exposed-infected-recovered (SEIR) disease spread models and their variants, thereby building credible and nonintrusive models to qualitatively model public health interventions at the population level.