Predicting spillover and emergence in an inherently stochastic world

Outbreaks of many severe infectious diseases such as Ebola or SARS result from spillover of pathogens from animals to humans. Fortunately, most such spillover events are rare, but, unfortunately, this means they are highly influenced by stochasticity and therefore hard to predict. One way to address this problem is by considering a larger, macroecological, spatiotemporal scale, but at a cost: the larger the scale, the less precise the predictions. I will discuss at which scales stochasticity might act on spillover and emergence, what this implies for predictability, and whether we need a sub-field-specific neutral theory of disease macroecology.