Infected or informed? Social structure and the simultaneous transmission of information and infectious disease

Evans, Julian C.; Silk, Matthew J.; Boogert, Neeltje J.; Hodgson, David J.

doi:10.1111/oik.07148

Cited by 41 publications

(65 citation statements)

References 192 publications

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“…Recent work has considered how the spread of information, or behaviours, may depend on the fine‐scale transmission mode between individuals, often using multiple predictor networks; disease ecology studies aiming to differentiate pathogen transmission mode could benefit by building on the methodology established in these studies (Hasenjager et al., 2020). Indeed, recent studies have developed tools to do so (Hasenjager et al., 2020; Sah, Otterstatter, et al., 2018), and their further popularisation may contribute to developing general theory comparing and contrasting the transmission of information and disease (Evans et al., 2020; Romano et al., 2020).…”

Section: Benefits Of Spatial‐social Network Analysismentioning

confidence: 99%

Unifying spatial and social network analysis in disease ecology

Albery

Kirkpatrick

Firth

et al. 2020

Journal of Animal Ecology

101

126

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1. Social network analysis has achieved remarkable popularity in disease ecology, and is sometimes carried out without investigating spatial heterogeneity. Many investigations into sociality and disease may nevertheless be subject to cryptic spatial variation, so ignoring spatial processes can limit inference regarding disease dynamics. 2. Disease analyses can gain breadth, power and reliability from incorporating both spatial and social behavioural data. However, the tools for collecting and analysing these data simultaneously can be complex and unintuitive, and it is often unclear when spatial variation must be accounted for. These difficulties contribute to the scarcity of simultaneous spatial-social network analyses in disease ecology thus far. 3. Here, we detail scenarios in disease ecology that benefit from spatial-social analysis. We describe procedures for simultaneous collection of both spatial and social data, and we outline statistical approaches that can control for and estimate spatial-social covariance in disease ecology analyses. 4. We hope disease researchers will expand social network analyses to more often include spatial components and questions. These measures will increase the scope of such analyses, allowing more accurate model estimates, better inference of transmission modes, susceptibility effects and contact scaling patterns, and ultimately more effective disease interventions. K E Y W O R D S disease ecology, methodology, parasite transmission, social network analysis, spatial analysis GPS: animals are marked and tracked over relatively large distances using satellites. Spatial: summarise individuals' movements across the landscape. Social: parameterise activity patterns to identify groups or interactions.

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Section: Benefits Of Spatial‐social Network Analysismentioning

confidence: 99%

Unifying spatial and social network analysis in disease ecology

Albery

Kirkpatrick

Firth

et al. 2020

Journal of Animal Ecology

101

126

View full text Add to dashboard Cite

show abstract

“…when contacts between groups or modules are infrequent but not rare) due to strong social reinforcement within groups (Nematzadeh et al 2014). As a result, modular networks may provide one route to promote the spread of information through a population disproportionately to the spread of infection (Evans et al 2020). However, there are many other aspects of the structure of modular networks that might also shape this pattern and have been less well studied.…”

Section: Introductionmentioning

confidence: 99%

“…However, interacting with others can also be costly, with one of the primary costs being the risk of infection with pathogens or parasites (Daszak et al 2000; Stattner and Vidot 2011). Balancing risk of infection with the potential to gain useful information is therefore one of the key trade-offs facing animals that engage in social interactions (Evans et al 2020; Romano et al 2020). In order to better understand this trade-off, it is necessary to quantify how different social structures can promote access to useful information while minimising risk of infection (Evans et al 2020; Firth 2020; Romano et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Balancing risk of infection with the potential to gain useful information is therefore one of the key trade-offs facing animals that engage in social interactions (Evans et al 2020; Romano et al 2020). In order to better understand this trade-off, it is necessary to quantify how different social structures can promote access to useful information while minimising risk of infection (Evans et al 2020; Firth 2020; Romano et al 2020). Individuals occupying particular positions in these social structures can have higher fitness (Cameron et al 2009; Wey et al 2013) and certain social structures can also help maximise the benefits of sociality for their members, promoting the persistence of social units (Ilany and Akcay 2016; Kramer and Meunier 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Social networks offer a useful toolkit to capture and quantify this diversity (Krause et al 2007; Wey et al 2008; Pinter-Wollman et al 2014), and can help distinguish species that, for example, live in multi-level societies (VanderWaal et al 2014; Cantor et al 2015; Papageorgiou et al 2019), stable social groups (Weinrich 1991; Wey et al 2013; Shizuka et al 2014), fission-fusion groups (Kerth and Konig 1999; Couzin and Laidre 2009; Silk et al 2014), or whose interactions and associations are more constrained by other factors such as shared space or resource use (Davis et al 2015; Spiegel et al 2016; Evans and Morand-Ferron 2019). Social network structure plays a critical role in governing both pathogen and information transmission (Aplin et al 2012; Godfrey 2013; Webster et al 2013; Silk et al 2017; Evans et al 2020). For example, variability in the connectedness of individuals can drive a superspreader effect in infectious diseases, causing more explosive outbreaks (Lloyd-Smith et al 2005).…”

Section: Introductionmentioning

confidence: 99%

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Group size and modularity interact to shape the spread of infection and information through animal societies

Evans

Hodgson

Boogert

et al. 2021

Preprint

Self Cite

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Social interactions between animals can provide many benefits, including the ability to gain useful environmental information through social learning. However, these social contacts can also facilitate the transmission of infectious diseases through a population. Animals engaging in social interactions must therefore face a trade-off between the potential informational benefits and the risk of acquiring disease. In order to understand how this trade-off can influence animal sociality, it is necessary to quantify the effects of different social structures on individuals’ likelihood of acquiring information versus infection Theoretical models have suggested that modular social networks, associated with the formation of groups or sub-groups, can slow spread of infection by trapping it within particular groups. However these social structures will not necessarily impact the spread of information in the same way if its transmission is considered as a “complex contagion”, e.g. through individuals copying the majority (conformist learning). Here we use simulation models to demonstrate that modular networks can promote the spread of information relative to the spread of infection, but only when the network is fragmented and group sizes are small. We show that the difference in transmission between information and disease is maximised for more well-connected social networks when the likelihood of transmission is intermediate. Our results have important implications for understanding the selective pressures operating on the social structure of animal societies, revealing that highly fragmented networks such as those formed in fission-fusion social groups and multilevel societies can be effective in modulating the infection-information trade-off for individuals within them.Significance statementRisk of infection is commonly regarded as one of the costs of animal social behaviours, while the potential for acquiring useful information is seen as a benefit. Balancing this risk of infection with the potential to gain useful information is one of the key trade-offs facing animals that engage in social interactions. In order to better understand this trade-off, it is necessary to quantify how different social structures can promote access to useful information while minimising risk of infection. We used simulations of disease and information spread to examine how group sizes and social network fragmentation influences both these transmission processes. Our models find that more subdivided networks slow the spread of disease far more than infection, but only group sizes are small. Our results demonstrate that showing that fragmented social structures can be more effective in balancing the infection-information trade-off for individuals within them.

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Social situations differ in their contribution to population‐level social structure in griffon vultures

Sharma

Anglister

Spiegel

et al. 2023

Ecology and Evolution

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Social relationships among animals emerge from interactions in multiple ecological and social situations. However, we seldom ask how each situation contributes to the global structure of a population, and whether different situations contribute different information about social relationships and the position of individuals within the social fabric. Griffon vultures (Gyps fulvus) interact socially in multiple situations, including communal roosting, joint flights, and co‐feeding. These social interactions can influence population‐level outcomes, such as disease transmission and information sharing that determine survival and response to changes. We examined the unique contribution of each social and ecological situation to the social structure of the population and individuals' positions within the overall social network using high‐resolution GPS tracking. We found that the number of individuals each vulture interacted with (degree) was best predicted by diurnal interactions—both during flights and on the ground (such as when feeding). However, the strength of social bonds, that is, the number of interactions an individual had (strength), was best predicted by interactions on the ground—both during the day (e.g., while feeding) and at night (e.g., while roosting) but not by interactions while flying. Thus, social situations differ in their impact on the relationships that individuals form. By incorporating the ecological situations in which social interactions occur we gain a more complete view of how social relationships are formed and which situations are important for different types of interactions.

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Infected or informed? Social structure and the simultaneous transmission of information and infectious disease

Cited by 41 publications

References 192 publications

Unifying spatial and social network analysis in disease ecology

Unifying spatial and social network analysis in disease ecology

Group size and modularity interact to shape the spread of infection and information through animal societies

Social situations differ in their contribution to population‐level social structure in griffon vultures

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