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A Provocation: Social insects as an experimental model of network epidemiology

A Provocation: Social insects as an experimental model of network epidemiology. Michael Otterstatter (CA). Modeling disease dynamics. e.g., the SIR model. Of course, in real host populations patterns of contact are heterogeneous….

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A Provocation: Social insects as an experimental model of network epidemiology

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  1. A Provocation: Social insects as an experimental model of network epidemiology Michael Otterstatter (CA)

  2. Modeling disease dynamics e.g., the SIR model Of course, in real host populations patterns of contact are heterogeneous… Traditional approach – compartmental models; homogeneous host population, complete mixing

  3. Modeling disease dynamics A more recent approach – network models; individual-based, patterns of contact are modeled explicitly Erdos-Renyi random graph Primary focus has been theoretical network structures; few empirical studies exist Poisson network How might we test if network models capture the epidemiology of real host populations?

  4. Social insects leafcutter ants honey bees bumble bees amenable model of disease dynamics • Social group size and transmission in ants (Hughes et al, 2002) • Infectiousness and transmission in honey bees (Naug & Smith, 2006) • Contact network structure and transmission in bumble bees (Otterstatter & Thomson, 2007)

  5. Experimental epidemiology with bees Quantifying social networks: Digital camcorder Foraging arena with feeder Behavioural tracking software Bee colony Introducing pathogens into social networks: Inoculation during foraging Donors (infected bees) Natural bee pathogens

  6. Experimental epidemiology with bees Quantifying social networks: Digital camcorder Foraging arena with feeder Behavioural tracking software Bee colony Introducing pathogens into social networks: Inoculation during foraging Donors (infected bees) Tracers may be artificial ! Natural bee pathogens

  7. Experimental epidemiology with bees Forager Nest worker Forager Nest worker Nest worker Nest worker Queen Example of an observed interaction network (node diameter ≈ degree centrality; edge weight ≈ contact rate) Artificially infected bee Example of an observed transmission network (node diameter ≈ risk of infection; edge weight ≈ transmission rate)

  8. Simple (but useful) tests of network theory, using bumblejbees Within groups, disease spreads more quickly when network density is high (each point = 1 hive) An individual’s risk of infection depends on its unique rate of contact with infecteds, i.e., its position in the social network (each point = 1 bee) …from Otterstatter & Thomson, 2007

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