Matthew J. Silk

Individual host behaviours can drastically impact the spread of infection through a population. Differences in the value individuals place on both socializing with others and avoiding infection have been shown to yield emergent homophily in social networks and thereby shape epidemic outcomes. We build on this understanding to explore how individuals who do not conform to their social surroundings contribute to the propagation of infection during outbreaks. We show how non-conforming individuals, even if they do not directly expose a disproportionate number of other individuals themselves, can become functional superspreaders through an emergent social structure that positions them as the functional links by which infection jumps between otherwise separate communities. Our results can help estimate the potential success of real-world interventions that may be compromised by a small number of non-conformists if their impact is not anticipated, and plan for how best to mitigate their effects on intervention success.

Many pathogens of public health and conservation concern persist in host communities. Identifying candidate maintenance and reservoir species is therefore a central component of disease management. The term maintenance species implies that if all species but the putative maintenance species were removed, then the pathogen would still persist. In the absence of field manipulations, this statement inherently requires a causal or mechanistic model to assess. However, we lack a systematic understanding of (i) how often conclusions are made about maintenance and reservoir species without reference to mechanistic models (ii) what types of biases may be associated with these conclusions and (iii) how explicitly invoking causal or mechanistic modelling can help ameliorate these biases. Filling these knowledge gaps is critical for robust inference about pathogen persistence and spillover in multihost–parasite systems, with clear implications for human and wildlife health. To address these gaps, we performed a literature review on the evidence previous studies have used to make claims regarding maintenance or reservoir species. We then developed multihost–parasite models to explore and demonstrate common biases that could arise when inferring maintenance potential from observational prevalence data. Finally, we developed new theory to show how model‐driven inference of maintenance species can minimize and eliminate emergent biases. In our review, we found that 83% of studies used some form of observational prevalence data to draw conclusions on maintenance potential and only 6% of these studies combined observational data with mechanistic modelling. Using our model, we demonstrate how the community, spatial and temporal context of observational data can lead to substantial biases in inferences of maintenance potential. Importantly, our theory identifies that model‐driven inference of maintenance species elucidates other streams of observational data that can be leveraged to correct these biases. Model‐driven inference is an essential, yet underused, component of multidisciplinary studies that make inference about host reservoir and maintenance species. Better integration of wildlife disease surveillance and mechanistic models is necessary to improve the robustness and reproducibility of our conclusions regarding maintenance and reservoir species.