Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis)

Abstract

Summary 1.Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. 2. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. 3. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. 4. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. 5. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.

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“…In our parasite-sharing transmission networks, the meaning of an edge is not equivalent to networks constructed based on contact patterns. Whereas contact networks represent potential for transmission based on co-occurrence in space and time, we follow others [23,24] by assuming that edges in our transmission networks depict the potential for transmission between a pair of individuals of the same or different host species based on ecological and physiological characteristics that promote parasite sharing. By transmission potential, we mean the likelihood that a given individual will infect another individual, relative to other individuals in the network, based on observed parasite sharing.…”

“…A third limitation is that TPNs may not represent true individual contacts. However, a recent study by VanderWaal et al [23] showed that a network based on shared E. coli strains co-varied with a network of social contacts in giraffes (Giraffa camelopardalis). This finding supports the assumption in our study (and in their later study on multi-host networks [24]) that transmission pathways based on shared parasites can reflect transmission pathways based on social contacts.…”

“…Our general approach is to project the bipartite host-parasite networks to unipartite networks by connecting two host individuals if they share at least one parasite species (Fig. 1C,D) [23,24]. Although using networks based on parasite sharing can yield important insights into the relationship between network structure and possible transmission patterns [23,24], a complete picture of the system requires an analysis of both the host-parasite and the (projected) transmission networks, because the structure of the latter is a direct result of the structure of the former.…”

“…1C,D) [23,24]. Although using networks based on parasite sharing can yield important insights into the relationship between network structure and possible transmission patterns [23,24], a complete picture of the system requires an analysis of both the host-parasite and the (projected) transmission networks, because the structure of the latter is a direct result of the structure of the former. In addition, the effect of the inclusion of multiple hosts in such networks on parasite dynamics has not been investigated.…”

Social dynamics, network structure, and information diffusion in fish shoals.

“…The ability of SNA to integrate individual behavior and population structure allows for a more sophisticated exploration of questions at both levels; many behaviors can only be fully understood when placed within the social context of the entire population. For example, the spread of social information, diseases, or parasites through a population depends not only on whom an individual directly interacts with, but also with whom their social partners interact (Godfrey et al, 2009;VanderWaal, Atwill, et al, 2014).…”