Although the approach of contact network epidemiology has been increasing in popularity for studying transmission of infectious diseases in human populations, it has generally been an underutilized approach for investigating disease outbreaks in wildlife populations. In this paper we explore the differences between the type of data that can be collected on human and wildlife populations, provide an update on recent advances that have been made in wildlife epidemiology by using a network approach, and discuss why networks might have been underutilized and why networks could and should be used more in the future. We conclude with ideas for future directions and a call for field biologists and network modelers to engage in more cross-disciplinary collaboration.

Summary: Heterogeneity in host association patterns can alter pathogen transmission and strategies for control. Great apes are highly social and endangered animals that have experienced substantial population declines from directly transmitted pathogens; as such, network approaches to quantify contact heterogeneity could be crucially important for predicting infection probability and outbreak size following pathogen introduction, especially owing to challenges in collecting real‐time infection data for endangered wildlife. We present here the first study using network analysis to quantify contact heterogeneity in wild apes, with applications for predicting community‐wide infectious disease risk. Specifically, within a wild chimpanzee community, we ask how associations between individuals vary over time, and we identify traits of highly connected individuals that might contribute disproportionately to pathogen spread. We used field observations of behavioural encounters in a habituated wild chimpanzee community in Kibale National Park, Uganda to construct monthly party level (i.e. subgroup) and close‐contact (i.e. ≤5 m) association networks over a 9‐month period. Network analysis revealed that networks were highly dynamic over time. In particular, oestrous events significantly increased pairwise party associations, suggesting that community‐wide disease outbreaks should be more likely to occur when many females are in oestrus. Bayesian models and permutation tests identified traits of chimpanzees that were highly connected within the network. Individuals with large families (i.e. mothers and their juveniles) that range in the core of the community territory and to a lesser extent high‐ranking males were central to association networks, and thus represent the most important individuals to target for disease intervention strategies. Overall, we show striking temporal variation in network structure and traits that predict association patterns in a wild chimpanzee community. These empirically‐derived networks can inform dynamic models of pathogen transmission and have practical applications for infectious disease management of endangered wildlife species.

Many endangered wildlife populations are vulnerable to infectious diseases for which vaccines exist; yet, pragmatic considerations often preclude large-scale vaccination efforts. These barriers could be reduced by focusing on individuals with the highest contact rates. However, the question then becomes whether targeted vaccination is sufficient to prevent large outbreaks. To evaluate the efficacy of targeted wildlife vaccinations, we simulate pathogen transmission and control on monthly association networks informed by behavioural data from a wild chimpanzee community (KanyawaraN= 37, Kibale National Park, Uganda). Despite considerable variation across monthly networks, our simulations indicate that targeting the most connected individuals can prevent large outbreaks with up to 35% fewer vaccines than random vaccination. Transmission heterogeneities might be attributed to biological differences among individuals (e.g. sex, age, dominance and family size). Thus, we also evaluate the effectiveness of a trait-based vaccination strategy, as trait data are often easier to collect than interaction data. Our simulations indicate that a trait-based strategy can prevent large outbreaks with up to 18% fewer vaccines than random vaccination, demonstrating that individual traits can serve as effective estimates of connectivity. Overall, these results suggest that fine-scale behavioural data can help optimize pathogen control efforts for endangered wildlife.