An Agent-Based Approach for Modeling Dynamics of Contagious Disease Spread

Background: The propagation of communicable diseases through a population is an inherentspatial and temporal process of great importance for modern society. For this reason a spatiallyexplicit epidemiologic model of infectious disease is proposed for a greater understanding of thedisease's spatial diffusion through a network of human contacts.Objective: The objective of this study is to develop an agent-based modelling approach theintegrates geographic information systems (GIS) to simulate the spread of a communicable diseasein an urban environment, as a result of individuals' interactions in a geospatial context.Methods: The methodology for simulating spatiotemporal dynamics of communicable diseasepropagation is presented and the model is implemented using measles outbreak in an urbanenvironment as a case study. Individuals in a closed population are explicitly represented by agentsassociated to places where they interact with other agents. They are endowed with mobility,through a transportation network allowing them to move between places within the urbanenvironment, in order to represent the spatial heterogeneity and the complexity involved ininfectious diseases diffusion. The model is implemented on georeferenced land use dataset fromMetro Vancouver and makes use of census data sets from Statistics Canada for the municipality ofBurnaby, BC, Canada study site.Results: The results provide insights into the application of the model to calculate ratios ofsusceptible/infected in specific time frames and urban environments, due to its ability to depict thedisease progression based on individuals' interactions. It is demonstrated that the dynamic spatialinteractions within the population lead to high numbers of exposed individuals who performstationary activities in areas after they have finished commuting. As a result, the sick individuals areconcentrated in geographical locations like schools and universities.Conclusion: The GIS-agent based model designed for this study can be easily customized to studythe disease spread dynamics of any other communicable disease by simply adjusting the modeleddisease timeline and/or the infection model and modifying the transmission process. This type ofsimulations can help to improve comprehension of disease spread dynamics and to take bettersteps towards the prevention and control of an epidemic outbreak.