The application of networks in infectious disease modelling provides insight into the epidemiological process that occurs in society, as network models allow for a natural disease progression throughout the population. In addition, network models provide the opportunity for greater heterogeneity and detail to be incorporated at the individual level, which can have a substantial influence on the spread of the disease and the success of an intervention. For example, super-spreaders have a large impact on the spread of disease and by targeting these individuals for vaccination, public health can efficiently reduce the spread of disease compared to random vaccination of the population. However, we found that individual behaviour can undermine the maximum potential of a targeted vaccination strategy.
Currently, West Africa is experiencing the worst Ebola outbreak in history.This outbreak has affected both urban and rural communities, which have distinguishable social structures. We found that ring vaccination could provide substantial benefit to case isolation in reducing mortality in both of these settings. In particular, we found that ring vaccination can be a valuable asset to case isolation when contact tracing is logistically challenging. The work discussed will show that identifying the optimal control strategy for a disease is not always clear because of the influence of social network structure and behaviour. Understanding the impact of social network structure and behaviour on disease interventions is important in determining the most efficient and effective control strategies.
Dr. Chad Wells graduated with his MSc degree from the University of Waterloo and his PhD from the Department of Mathematics and Statistics at the University of Guelph. Currently, he is with the Yale School of Medicine, working in epidemiology and focusing on microbial diseases.