On the other hand, group living is also associated with the costs of elevated disease burden due to higher frequency of contact between hosts [2]. Recently, it has been suggested that the formation of subgroups mitigates the association between group size and disease burden [3, 4]. Subdivided social groups are expected to experience lower outbreak size as compared to societies with no subgroups by acting as "transmission bottlenecks" to the invading pathogen …show more content…
In fact, completely mixed social networks where each individual interacts equally with every other individual in the group are rare and most demonstrate at least some degree of compartmentalization.(Delete? not so rare)
The origins of modular organization in animal societies is based on pairwise social relationships, demography, environ- mental and landscape factors. The role of social relationships in formation of modular patterns is evident in species demon- strating fission-fusion behavior, where social groups frequently split into subgroups and fuse again. Social network of these species resembles a random network with no subdivision during their fusion state and modular during the fission state [13]. In addition, modular configuration in social groups is affected by turnover in social bonds due to demographic changes in a so- cial group, caused by birth/death or immigration/emigration of individuals [14]. Environmental factors such as seasonal changes, food or water availability can further create or re- duce the opportunities of contact which may also influence the modular organization in animal groups [15, 16]. …show more content…
This is impor- tant because not all animal social networks exhibit high level of modular subdivisions typically investigated in theoretical studies. If disease spread is unaffected by the observed levels of modular organization in animal societies, it would imply that its knowledge is not necessary to predict epidemiological consequences of animal social organization. This information is critical, as being a data-intensive approach, network models that requisite the least amount of data collection tend to be more efficient.
In this paper we attempt to achieve three objectives: 1) identify structural factors that increase the strength modular organization in animal social groups, 2) explore the conditions under which the modular organization influences the dynam- ics of disease spread and explain the mechanism behind the observed effect, and 3) investigate whether modular subdi- visions play an important role in predicting disease burden as compared to other mesoscopic structures in animal social networks. For the first objective, we used empirical