How disease models in static networks can fail to approximate disease in dynamic networks.
In the modeling of infectious disease spread within explicit social contact networks, previous studies have predominantly assumed that the effects of shifting social associations within groups are small. These models have utilized static approximations of contact networks. We examine this assumption by modeling disease spread within dynamic networks where associations shift according to individual preference based on three different measures of network centrality. The results of our investigations clearly show that this assumption may not hold in many cases. We demonstrate that these differences in association dynamics do yield significantly different disease outcomes both from each other and also from models using graph-theoretically accurate static network approximations. Further work is therefore needed to explore under which circumstances static models accurately reflect constantly shifting natural populations.
Fefferman
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InForMID, Tufts University School of Medicine, 136 Harrison Avenue, Boston, Massachusetts 02111, USA.
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