63 research outputs found
Effects of Contact Network Models on Stochastic Epidemic Simulations
The importance of modeling the spread of epidemics through a population has
led to the development of mathematical models for infectious disease
propagation. A number of empirical studies have collected and analyzed data on
contacts between individuals using a variety of sensors. Typically one uses
such data to fit a probabilistic model of network contacts over which a disease
may propagate. In this paper, we investigate the effects of different contact
network models with varying levels of complexity on the outcomes of simulated
epidemics using a stochastic Susceptible-Infectious-Recovered (SIR) model. We
evaluate these network models on six datasets of contacts between people in a
variety of settings. Our results demonstrate that the choice of network model
can have a significant effect on how closely the outcomes of an epidemic
simulation on a simulated network match the outcomes on the actual network
constructed from the sensor data. In particular, preserving degrees of nodes
appears to be much more important than preserving cluster structure for
accurate epidemic simulations.Comment: To appear at International Conference on Social Informatics (SocInfo)
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