  Empirical studies show that epidemiological models based on an epidemic's
initial spread rate often fail to predict the true scale of that epidemic. Most
epidemics with a rapid early rise die out before affecting a significant
fraction of the population, whereas the early pace of some pandemics is rather
modest. Recent models suggest that this could be due to the heterogeneity of
the target population's susceptibility. We study a computer malware ecosystem
exhibiting spread mechanisms resembling those of biological systems while
offering details unavailable for human epidemics. Rather than comparing models,
we directly estimate reach from a new and vastly more complete data from a
parallel domain, that offers superior details and insight as concerns
biological outbreaks. We find a highly heterogeneous distribution of computer
susceptibilities, with nearly all outbreaks initially over-affecting the tail
of the distribution, then collapsing quickly once this tail is depleted. This
mechanism restricts the correlation between an epidemic's initial growth rate
and its total reach, thus preventing the majority of epidemics, including
initially fast-growing outbreaks, from reaching a macroscopic fraction of the
population. The few pervasive malwares distinguish themselves early on via the
following key trait: they avoid infecting the tail, while preferentially
targeting computers unaffected by typical malware.
