Data from: Stochasticity and infectious disease dynamics: density and weather effects on a fungal insect pathogen

In deterministic models of epidemics, there is a host abundance threshold,
above which the introduction of a few infected individuals leads to a
severe epidemic. Studies of weather-driven animal pathogens often assume
that abundance thresholds will be overwhelmed by weather-driven
stochasticity, but tests of this assumption are lacking. We collected
observational and experimental data for a fungal pathogen, {\it
Entomophaga maimaiga}, that infects the gypsy moth, {\it Lymantria
dispar}. We used an advanced statistical-computing algorithm to fit
mechanistic models to our data, such that different models made different
assumptions about the effects of host density and weather on {\it E.
maimaiga} epizootics (epidemics in animals). We then used AIC analysis to
choose the best model. In the best model, epizootics are driven by a
combination of weather and host density, and the model does an excellent
job of explaining the data, whereas models that allow only for weather
effects, or only density-dependence effects, do a poor job of explaining
the data. Density-dependent transmission in our best model produces a
host-density threshold, but this threshold is strongly blurred by the
stochastic effects of weather. Our work shows that host-abundance
thresholds may be important even if weather strongly affects transmission,
suggesting that epidemiological models that allow for weather have an
important role to play in understanding animal pathogens. The success of
our model means that it could be useful for managing the gypsy moth, an
important pest of hardwood forests in North America.