Evolving contact networks to analyze epidemic behaviour and studying the effects of vaccination

Shiller, Elisabeth
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University of Guelph

Epidemic models help researchers understand and predict the nature of a potential epidemic. This study analyzes and improves network evolution technology that evolves contact networks so that simulated epidemics on the network mimic a specified epidemic pattern. The evolutionary algorithm incorporates the novel recentering-restarting algorithm, which is adopted into the optimizer to allow for efficient search of the space of networks. It also implements the toggle-delete representation which allows for broader search of solution space. Then, a diffusion character based method is used for analyzing the contact networks. A comparison of simulated epidemics that result from changing patient zero for a single contact network is performed. It is found that the location of patient zero is important for the behaviour of an epidemic. The social fabric representation is invented and then tested for parameter choices. The response to vaccination strategies (including ring vaccination) is then tested by incorporating them into the epidemic simulations.

evolutionary computation, epidemiology, contact networks, vaccination, patient zero, diffusion character, recentering restarting, social fabric, ring vaccination, network topology, SIR model