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Empirical studies show that epidemiological models based on an epidemic'sinitial spread rate often fail to predict the true scale of that epidemic. Mostepidemics with a rapid early rise die out before affecting a significantfraction of the population, whereas the early pace of some pandemics is rathermodest. Recent models suggest that this could be due to the heterogeneity ofthe target population's susceptibility. We study a computer malware ecosystemexhibiting spread mechanisms resembling those of biological systems whileoffering details unavailable for human epidemics. Rather than comparing models,we directly estimate reach from a new and vastly more complete data from aparallel domain, that offers superior details and insight as concernsbiological outbreaks. We find a highly heterogeneous distribution of computersusceptibilities, with nearly all outbreaks initially over-affecting the tailof the distribution, then collapsing quickly once this tail is depleted. Thismechanism restricts the correlation between an epidemic's initial growth rateand its total reach, thus preventing the majority of epidemics, includinginitially fast-growing outbreaks, from reaching a macroscopic fraction of thepopulation. The few pervasive malwares distinguish themselves early on via thefollowing key trait: they avoid infecting the tail, while preferentiallytargeting computers unaffected by typical malware.

Initial growth rates of malware epidemics fail to predict their reach