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Contagion in real social networks: insights from social insects Michael Otterstatter Zachary Jacobson

Contagion in real social networks: insights from social insects

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Contagion in real social networks: insights from social insects. Michael Otterstatter Zachary Jacobson. What is a social network?. Influence. Disease. Information. Resources. Disease spread in social networks. Meyers et al. 2005. J. Theor. Biol. WHO 2005. - PowerPoint PPT Presentation

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Page 1: Contagion in real social networks: insights from social insects

Contagion in real social networks:insights from social insects

Michael Otterstatter

Zachary Jacobson

Page 2: Contagion in real social networks: insights from social insects

What is a social network?

Information

Resources

Disease

Influence

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Page 3: Contagion in real social networks: insights from social insects

Disease spread in social networks

WHO 2005

Meyers et al. 2005. J. Theor. Biol.

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Page 4: Contagion in real social networks: insights from social insects

Problem: disease spread is unobservable

A possible solution: study transmission of observable proxies for contagious disease

▫ infectious spread of behaviour (behavioural contagion)

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Page 5: Contagion in real social networks: insights from social insects

A novel approach

The transmission of behaviour, as a proxy for disease, can be studied directly in social insect networks

Here, we ask

•does mobility behaviour spread contagiously among bumble bees via social contact?

•is the contagious spread of behaviour a useful proxy for the spread of disease?

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Page 6: Contagion in real social networks: insights from social insects

Materials and methods

•Bumble bees (Bombus spp.)▫7 colonies, reared from wild queens▫colonies maintained in the lab under constant light,

temperature▫bees allowed to forage at will in flight cage ▫observations throughout colony cycle (3-20+ bees)

•Automated behavioural tracking▫Ethovision software used for 331 hr hive observations,

tracking movement and contacts between nestmates▫all observations and analyses are based on the natural

behaviour of bees within their hive

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Page 7: Contagion in real social networks: insights from social insects

Lifecycle of bumble bees

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Page 8: Contagion in real social networks: insights from social insects

Bumble bees in the lab

‘bee-movie’

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Page 9: Contagion in real social networks: insights from social insects

Automated tracking of bee behaviour5 cm

Example of movement traces from a single colony

colony

flight cage

behavioural trackingsoftware

videocamera

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Page 10: Contagion in real social networks: insights from social insects

Three analyses of bee mobility behaviour

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Page 11: Contagion in real social networks: insights from social insects

1. Analysis of isolated bees

Do isolated inactive bees ‘activate’ spontaneously after a fixed interval?

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Page 12: Contagion in real social networks: insights from social insects

2. Analysis of interacting bees

contactrates

Are inactive bees ‘activated’ by contacts from mobile nestmates?

zzz

mobility behaviour

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Page 13: Contagion in real social networks: insights from social insects

3. Analysis of all bees within a hive

In an active hive, is a bee’s movement behaviour related to its recent contact rate with nestmates?

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Page 14: Contagion in real social networks: insights from social insects

Results of bee behaviour analysis

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Page 15: Contagion in real social networks: insights from social insects

1. Mobility behaviour of isolated bees

Isolated bees show no inherent rhythm of activity

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Page 16: Contagion in real social networks: insights from social insects

2. Mobility behaviour of interacting bees

Inactive bees

…that became active (n=89) …that remained inactive (n=21)

rec’d 1.46 contacts/min rec’d 0.14 contacts/min

Inactive bees receiving many contacts from mobile nestmates tend to become mobile themselves

zzz

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Page 17: Contagion in real social networks: insights from social insects

P = 0.03

P = 0.001

After a ‘refractory’ period, contacts from nestmates increase a bee’s probability of becoming mobile(logistic regression)

2. Mobility behaviour of interacting bees zzz

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Page 18: Contagion in real social networks: insights from social insects

3. Mobility behaviour of all nestmates

Granger Causality Statistics

Uni-directional causalityContact causes

MobilityMobility causes

ContactsBi-directional

causality No causality

4 bees 5 bees 17 bees 8 bees

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In most cases, social contacts cause mobility behaviour to spread between bees and mobility feeds back to cause increased contacts (bi-directional causality)

(summary results from multivariate time-series analysis)

Page 19: Contagion in real social networks: insights from social insects

Predicted dynamics of groups

Simulated activity of social group (Goss & Deneubourg, 1988)

When individuals behave as we observed:

We expect group behaviour like this:

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Page 20: Contagion in real social networks: insights from social insects

Observed dynamics in bee hives

In bee hives, activity level showed stable cycles as predicted

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Page 21: Contagion in real social networks: insights from social insects

Observed dynamics in bee hives

Also, average rates of contact within hives showed stable cycles

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Page 22: Contagion in real social networks: insights from social insects

Spread of behaviour and disease

Importantly, these results suggest that the basic underlying ‘model’ of behavioural contagion and disease contagion may be the same:

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Behavioural contagion:

Disease contagion (SIR model):

Page 23: Contagion in real social networks: insights from social insects

Conclusions

•Mobility behaviour spreads contagiously among bumble bees through social contact

•Social transmission of mobility, like disease, results in oscillatory dynamics at group level

•Studying observable transmission of behaviour offers a way to understand the unobservable spread of disease in social networks

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Page 24: Contagion in real social networks: insights from social insects

Acknowledgements

Technical assistance:

Kieran Samuk, Athena Fung

Funding:

Health Canada Postdoctoral Fellowship Program

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