Pregnant women appear to be more susceptible to infectious diseases than women in reproductive age. According to the California Department of Public Health pregnant women were 9.6-folds more likely to be hospitalized during the 2009 influenza outbreak when compared to non-pregnant women in reproductive age. In contrast, it was reported that of 16,749 COVID-19 patients that were hospitalized in the UK, the probability for pregnant women to require in-patient care due to infection by SARS-CoV-2 was 0.95 versus non-pregnant women. Therefore 9.6/0.95 = 10.10, which brings us to the conclusion that pregnant women are 10.10-folds less likely to be hospitalized for a SARS-CoV-2 infection than for the 2009 H1N1 pandemic. Folic acid supplementation during pregnancy could be the factor that is protecting these patients against SARS-CoV-2 infection. Two independent papers that used informatic simulation proved that folic acid reduced the replication of this virus. One of them showed that folic acid inhibits the furin protease which the virus needs in order to enter its host cell, while the other one explained that folic acid inactivates protease 3CL pro , a protein that the virus needs to replicate. Nonetheless the probability that folic acid blocks two different proteins is very low, therefore the mechanism by which folic acid has apparently protected pregnant women during the COVID-19 pandemic has not been determined.

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Many growth models have been published to model the behavior of real complex networks. These models are able to reproduce several of the topological properties of such networks. However, in most of these growth models, the number of outgoing links (i.e., out-degree) of nodes added to the network is constant, that is all nodes in the network are born with the same number of outgoing links. In other models, the resultant out-degree distribution decays as a poisson or an exponential distribution. However, it has been found that in real complex networks, the out-degree distribution decays as a power-law. In order to obtain out-degree distribution with power-law behavior some models have been proposed. This work introduces a new model that allows to obtain out-degree distributions that decay as a power-law with an exponent in the range from 0 to 1.

Several real-world directed networks do not have multiple links. For example, in a paper citation network a paper does not cite two identical references, and in a network of friends there exists only a single link between two individuals. This suggest that the growth and evolution models of complex networks should take into account such feature in order to approximate the topological properties of this class of networks. The aim of this paper is to propose a growth model of directed complex networks that takes into account the prohibition of the existence multiple links. It is shown through numerical experiments that when multiple links are forbidden, the exponent γ of the in-degree connectivity distribution, [Formula: see text], takes values ranging from 1 to ∞. In particular, the proposed multi-link free (MLF) model is able to predict exponents occurring in real-world complex networks, which range 1.05 < γ < 3.51. As an example, the MLF reproduces somxe topological properties exhibited by the network of flights between airports of the world (NFAW); i.e. γ ≈ 1.74. With this result, we believe that the multiple links prohibition might be one of the local processes accounting for the existence of exponents γ < 2 found in some real complex networks.

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