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

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

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

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

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

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

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

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 Simple (but useful) tests of network theory, using bumblejbees