Background: The most severe bacterial disease of honeybees is American foulbrood (AFB). The epidemiology of AFB is driven by the extreme spore resilience, the difficulty of bees to remove these spores, and the considerable incidence of subclinical, spore-producing colonies. Collective defence and its feedback on colony development involves role allocation at multiple levels in colony organization and are difficult to model. To better predict disease outbreaks we need to understand the feedback between colony development and disease progression within the colony. We therefore developed Bayesian models with data from 40 infected colonies monitored over an entire foraging season to (i) investigate the spore-symptom relationship, (ii) disentangle the feedback loops between disease epidemiology and natural colony development, and (iii) discuss whether larger insect societies promote or limit within-colony disease transmission.
Results: Rather than identifying a fixed spore count threshold for clinical symptoms, we estimated the probabilities around the relationship between spore counts and symptoms, taking into account modulators such as brood amount/number of bees and time post infection. We identified a decrease over time in the bees-to-brood ratio related to disease development, which should ultimately induce colony collapse. Lastly, two contrasting theories predict that larger colonies could promote either higher (classical epidemiological SIR-model) or lower (increasing spatial nest segregation and more effective pathogen removal) disease prevalence.
Conclusions: AFB followed the predictions of the SIR-model, partly because the hygienic behaviour of brood removal and disease prevalence are decoupled with worker bees acting as disease vectors, infecting new brood. We therefore established a direct link between disease prevalence and social group size for a eusocial insect.