An agent-based approach for modeling dynamics of contagious disease spread

Abstract

Background: The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts.

Objective: The objective of this study is to develop an agent-based modelling approach the integrates geographic information systems (GIS) to simulate the spread of a communicable disease in an urban environment, as a result of individuals' interactions in a geospatial context.

Methods: The methodology for simulating spatiotemporal dynamics of communicable disease propagation is presented and the model is implemented using measles outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by agents associated 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 urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. The model is implemented on georeferenced land use dataset from Metro Vancouver and makes use of census data sets from Statistics Canada for the municipality of Burnaby, BC, Canada study site.

Results: The results provide insights into the application of the model to calculate ratios of susceptible/infected in specific time frames and urban environments, due to its ability to depict the disease progression based on individuals' interactions. It is demonstrated that the dynamic spatial interactions within the population lead to high numbers of exposed individuals who perform stationary activities in areas after they have finished commuting. As a result, the sick individuals are concentrated in geographical locations like schools and universities.

Conclusion: The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak.

Figure 1

Different states of the SEIR infection model, to simulate the progress of and epidemic in a human population. L_Pi: latency period, I_Pi: infectious period, t_i: first day that an individual is exposed to the virus for the first time, x_LP: number of days for an exposed individual to become infective, and x_IP: number of days for an individual to recover from the disease.

Figure 2

Flow diagram representing different infection phases.

Figure 3

Process of the epidemic spread AB model for a single time step representing daily activity of individuals' activities and their interactions in an urban environment.

Figure 4

(a) Geographic area: City of Burnaby, Canada, with the relevant land use classes; (b) Geographic space of individual's interactions; (c) Geospatial data inputs.

Figure 5

Flow diagram that characterizes daily activities of individuals within the city.

Figure 6

Flow diagram for the infection rules that describe the disease propagation among individuals at physically fixed location.

Figure 7

Graphical user interface (GUI) developed for the model implementation. Dots represent the individuals moving within an urban environment.

Figure 8

Spatial distribution of Susceptible-Exposed-Infected-Immune population in an urban area on two different days. (a) day 1, (b) day 10, (c) 20 and (d) day 30 for Scenario 1. The black circles represent the susceptible population; the black stars represent the exposed population; the black triangles represent the infected population and the black squares represent the recovered (immune) population.

Figure 9

Graphical representation of the disease spread progression comparison of the proportion of individuals in each health state through time.

Figure 10

Graphical representation of the variation in number of cases of Susceptible-Infected-Exposed-Recovered individuals within the population and for the simulation outcomes for four scenarios.

Figure 11

Graphical representation of model sensitivity to changes in rate of infection based on the population density. Simulation outcomes for scenarios A, B, C, and D.

Figure 12

Graphical representation of model sensitivity to changes in time spent for different activities. Simulation outcomes for scenarios E, F, and G.