Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics

Dynamics and Control of Diseases in Networks with Community Structure

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies

On the general theory of the origins of retroviruses

<p>Abstract</p> <p>Background</p> <p>The order retroviridae comprises viruses based on ribonucleic acids (RNA). Some, such as HIV and HTLV, are human pathogens. Newly emerged human retroviruses have zoonotic origins. As far as has been established, both repeated infections (themselves possibly responsible for the evolution of viral mutations <b>(Vm) </b>and host adaptability <b>(Ha)</b>); along with interplay between <it>inhibitors </it>and <it>promoters </it>of cell tropism, are needed to effect retroviral cross-species transmissions. However, the exact <it>modus operadi </it>of intertwine between these factors at molecular level remains to be established. Knowledge of such intertwine could lead to a better understanding of retrovirology and possibly other infectious processes. This study was conducted to derive the mathematical equation of a general theory of the origins of retroviruses.</p> <p>Methods and results</p> <p>On the basis of an arbitrarily non-Euclidian geometrical "thought experiment" involving the cross-species transmission of simian foamy virus (sfv) from a non-primate species <it>Xy </it>to <it>Homo sapiens </it>(<it>Hs</it>), initially excluding all social factors, the following was derived. At the port of exit from <it>Xy </it>(where the species barrier, SB, is defined by the <it>Index of Origin</it>, IO), sfv shedding is (1) enhanced by two transmitting tensors <b>(Tt)</b>, (i) virus-specific immunity (VSI) and (ii) evolutionary defenses such as APOBEC, RNA interference pathways, and (when present) expedited therapeutics (denoted e²D); and (2) opposed by the five accepting scalars <b>(At)</b>: (a) genomic integration hot spots, gIHS, (b) nuclear envelope transit <b>(</b>NMt) vectors, (c) virus-specific cellular biochemistry, VSCB, (d) virus-specific cellular receptor repertoire, VSCR, and (e) pH-mediated cell membrane transit, (↓_pHCMat). Assuming <b>As </b>and <b>Tt </b>to be independent variables, <b>IO = Tt/As</b>. The same forces acting in an opposing manner determine SB at the port of sfv entry (defined here by the <it>Index of Entry</it>, <b>IE = As/Tt</b>). Overall, If sfv encounters no unforeseen effects on transit between X<it>y </it>and <it>Hs</it>, then the square root of the combined index of sfv transmissibility (√<b>|RTI|) </b>is proportional to the product IO* IE (or ~Vm* Ha* ∑Tt*∑As*<b>Ω</b>), where <b>Ω </b>is the retrovirological constant and ∑ is a function of the ratio Tt/As or As/Tt for sfv transmission from <it>Xy </it>to <it>Hs</it>.</p> <p>Conclusions</p> <p>I present a mathematical formalism encapsulating the general theory of the origins of retroviruses. It summarizes the choreography for the intertwined interplay of factors influencing the probability of retroviral cross-species transmission: <b>Vm, Ha, Tt, As, </b>and <b>Ω</b>.</p