Data from: High-resolution contact networks of free-ranging domestic dogs Canis familiaris and implications for transmission of infection

Contact patterns strongly influence the dynamics of disease transmission
in both human and non-human animal populations. Domestic dogs Canis
familiaris are a social species and are a reservoir for several zoonotic
infections, yet few studies have empirically determined contact patterns
within dog populations. Using high-resolution proximity logging
technology, we characterised the contact networks of free-ranging domestic
dogs from two settlements (n = 108 dogs, covering >80% of the
population in each settlement) in rural Chad. We used these data to
simulate the transmission of an infection comparable to rabies and
investigated the effects of including observed contact heterogeneities on
epidemic outcomes. We found that dog contact networks displayed
considerable heterogeneity, particularly in the duration of contacts and
that the network had communities that were highly correlated with
household membership. Simulations using observed contact networks had
smaller epidemic sizes than those that assumed random mixing,
demonstrating the unsuitability of homogenous mixing models in predicting
epidemic outcomes. When contact heterogeneities were included in
simulations, the network position of the individual initially infected had
an important effect on epidemic outcomes. The risk of an epidemic
occurring was best predicted by the initially infected individual’s ranked
degree, while epidemic size was best predicted by the individual’s ranked
eigenvector centrality. For dogs in one settlement, we found that ranked
eigenvector centrality was correlated with range size. Our results
demonstrate that observed heterogeneities in contacts are important for
the prediction of epidemiological outcomes in free-ranging domestic dogs.
We show that individuals presenting a higher risk for disease transmission
can be identified by their network position and provide evidence that
observable traits hold potential for informing targeted disease management
strategies.