Initial growth rates of epidemics fail to predict their reach: A lesson from large scale malware spread analysis

Many epidemiological models predict high morbidity levels based on an epidemic's fast initial spread rate. However, most epidemics with a rapid early rise die out before affecting a significant fraction of the population. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. We find an extremely heterogeneous distribution of computer susceptibility, with nearly all outbreaks starting at its tail and collapsing quickly once it is exhausted. This mechanism annuls the correlation between an epidemic's initial growth rate and its total reach and prevents the majority of epidemics from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early by avoiding infecting the tail and preferentially targeting computers unaffected by typical malware.