Initial growth rates of malware epidemics fail to predict their reach

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.

Universal properties of the dynamics of the Covid-19 pandemics

We present evidence for existence of a universal lower bound for the initial growth rate of the epidemic curve of the SARS-CoV-2 coronavirus. This can be used to infer that, on average, an asymptomatic infected individual is infectious during 5.6 plus/minus 0.3 days. We further present evidence of an average time scale of 12 days for halving the number of new cases, or new deaths, during the extinction period of the first phase of the epidemic.

Possible Correlation between COVID-19 Contagion and Y-DNA Haplogroup R1b

In this work, the linear correlation between the initial growth rate of COVID-19 contagion and the average Y-DNA haplogroup percentages in different countries is computed. In the case of haplogroup R1b, a positive correlation with high confidence level is found. Utilizing the maximum R1b percentages in place of the average ones, a more significant result is obtained. Considering an extended R1b data set, correlations with even higher confidence level are found (p-values 3.94E-7 and 2.40E-9, respectively). Repeating the same procedure for the initial growth rate of deaths, similar results are obtained (p-values 9.17E-11 and 2.18E-12, respectively). Furthermore, the correlation of haplogroup R1b with cases and deaths per capita is calculated over a five-month period, obtaining comparable results (e.g. p-value 2.45E-17 on April 10th). The difference between the correlation with maximum R1b percentages and the correlation with average ones is decreasing over time. Finally, assuming the possible involvement of R1b carriers, three scenarios are outlined according to their passive or active role in the spread of the virus.

Two strategies underlying the trade-off of hepatitis C virus proliferation: stay-at-home or leaving-home?

Viruses proliferate through both genome replication inside infected cells and transmission to new target cells or to new hosts. Each viral genome molecule in infected cells is used either for amplifying the intracellular genome as a template (“stay-at-home strategy”) or for packaging into progeny virions to be released extracellularly (“leaving-home strategy”). The balance between these strategies is important for both initial growth and transmission of viruses. In this study, we used hepatitis C virus (HCV) as a model system to study the functions of viral genomic RNA in both RNA replication in cells and in progeny virus assembly and release. Using viral infection assays combined with mathematical modelling, we characterized the dynamics of two different HCV strains (JFH-1, a clinical isolate, and Jc1-n, a laboratory strain), which have different viral assembly and release characteristics. We found that 1.27% and 3.28% of JFH-1 and Jc1-n intracellular viral RNAs, respectively, are used for producing and releasing progeny virions. Analysis of the Malthusian parameter of the HCV genome (i.e., initial growth rate) and the number of de novo infections (i.e., initial transmissibility) suggests that the leaving-home strategy provides a higher level of initial transmission for Jc1-n, while, in contrast, the stay-at-home strategy provides a higher initial growth rate for JFH-1. Thus, theoretical-experimental analysis of viral dynamics enables us to better understand the proliferation strategies of viruses. Ours is the first study to analyze stay-leave trade-offs during the viral life cycle and their significance for viral proliferation.