Inductive Reasoning Games as Influenza Vaccination Models: Mean Field Analysis

We define and analyze an inductive reasoning game of voluntary yearly vaccination in order to establish whether or not a population of individuals acting in their own self-interest would be able to prevent influenza epidemics. We find that epidemics are rarely prevented. We also find that severe epidemics may occur without the introduction of pandemic strains. We further address the situation where market incentives are introduced to help ameliorating epidemics. Surprisingly, we find that vaccinating families exacerbates epidemics. However, a public health program requesting prepayment of vaccinations may significantly ameliorate influenza epidemics.Comment: 20 pages, 7 figure

Serving GODAE Data and Products to the Ocean Community

The Global Ocean Data Assimilation Experiment (GODAE [http:// www.godae.org]) has spanned a decade of rapid technological development. The ever-increasing volume and diversity of oceanographic data produced by in situ instruments, remote-sensing platforms, and computer simulations have driven the development of a number of innovative technologies that are essential for connecting scientists with the data that they need. This paper gives an overview of the technologies that have been developed and applied in the course of GODAE, which now provide users of oceanographic data with the capability to discover, evaluate, visualize, download, and analyze data from all over the world. The key to this capability is the ability to reduce the inherent complexity of oceanographic data by providing a consistent, harmonized view of the various data products. The challenges of data serving have been addressed over the last 10 years through the cooperative skills and energies of many individuals

Interplay between HIV/AIDS Epidemics and Demographic Structures Based on Sexual Contact Networks

In this article, we propose a network spread model for HIV epidemics, wherein each individual is represented by a node of the transmission network and the edges are the connections between individuals along which the infection may spread. The sexual activity of each individual, measured by its degree, is not homogeneous but obeys a power-law distribution. Due to the heterogeneity of activity, the infection can persistently exist at a very low prevalence, which has been observed in real data but can not be illuminated by previous models with homogeneous mixing hypothesis. Furthermore, the model displays a clear picture of hierarchical spread: In the early stage the infection is adhered to these high-risk persons, and then, diffuses toward low-risk population. The prediction results show that the development of epidemics can be roughly categorized into three patterns for different countries, and the pattern of a given country is mainly determined by the average sex-activity and transmission probability per sexual partner. In most cases, the effect of HIV epidemics on demographic structure is very small. However, for some extremely countries, like Botswana, the number of sex-active people can be depressed to nearly a half by AIDS.Comment: 23 pages, 12 figure

Supporting smart urban growth: successful investing in density

This report analyses the characteristics of ‘good density’ and begins to quantify the relation-ship between these characteristics, investor returns, and carbon emissions. We found that cities with good density – that is, dense development thoughtfully designed to promote a high quality of life – are likely to be more resilient and prosperous in the long term, and there-fore more likely to provide sustainable returns for investors, than cities without good density. Based on a quantitative analysis of 63 global cities, the report finds that cities with good density are associated with higher returns, capital values, and levels of investment flows for commercial real estate. The research provides evidence of important issues for the long-term resilience of cities both in the OECD and in fast-growing developing regions

A Dynamic Model of Interactions of Ca^(2+), Calmodulin, and Catalytic Subunits of Ca^(2+)/Calmodulin-Dependent Protein Kinase II

During the acquisition of memories, influx of Ca^(2+) into the postsynaptic spine through the pores of activated N-methyl-D-aspartate-type glutamate receptors triggers processes that change the strength of excitatory synapses. The pattern of Ca^(2+) influx during the first few seconds of activity is interpreted within the Ca^(2+)-dependent signaling network such that synaptic strength is eventually either potentiated or depressed. Many of the critical signaling enzymes that control synaptic plasticity, including Ca^(2+)/calmodulin-dependent protein kinase II (CaMKII), are regulated by calmodulin, a small protein that can bind up to 4 Ca^(2+) ions. As a first step toward clarifying how the Ca^(2+)-signaling network decides between potentiation or depression, we have created a kinetic model of the interactions of Ca^(2+), calmodulin, and CaMKII that represents our best understanding of the dynamics of these interactions under conditions that resemble those in a postsynaptic spine. We constrained parameters of the model from data in the literature, or from our own measurements, and then predicted time courses of activation and autophosphorylation of CaMKII under a variety of conditions. Simulations showed that species of calmodulin with fewer than four bound Ca^(2+) play a significant role in activation of CaMKII in the physiological regime, supporting the notion that processing ofCa^(2+) signals in a spine involves competition among target enzymes for binding to unsaturated species of CaM in an environment in which the concentration of Ca^(2+) is fluctuating rapidly. Indeed, we showed that dependence of activation on the frequency of Ca^(2+) transients arises from the kinetics of interaction of fluctuating Ca^(2+) with calmodulin/CaMKII complexes. We used parameter sensitivity analysis to identify which parameters will be most beneficial to measure more carefully to improve the accuracy of predictions. This model provides a quantitative base from which to build more complex dynamic models of postsynaptic signal transduction during learning

Creating a proof-of-concept climate service to assess future renewable energy mixes in Europe: an overview of the C3S ECEM project

The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) has produced, in close collaboration with prospective users, a proof-of-concept climate service, or Demonstrator, designed to enable the energy industry and policy makers assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. The concept of C3S ECEM, its methodology and some results are presented here. The first part focuses on the construction of reference data sets for climate variables based on the ERA-Interim reanalysis. Subsequently, energy variables were created by transforming the bias-adjusted climate variables using a combination of statistical and physically-based models. A comprehensive set of measured energy supply and demand data was also collected, in order to assess the robustness of the conversion to energy variables. Climate and energy data have been produced both for the historical period (1979–2016) and for future projections (from 1981 to 2100, to also include a past reference period, but focusing on the 30 year period 2035–2065). The skill of current seasonal forecast systems for climate and energy variables has also been assessed. The C3S ECEM project was designed to provide ample opportunities for stakeholders to convey their needs and expectations, and assist in the development of a suitable Demonstrator. This is the tool that collects the output produced by C3S ECEM and presents it in a user-friendly and interactive format, and it therefore constitutes the essence of the C3S ECEM proof-of-concept climate service

Predicting the emergence of drug-resistant HSV-2: new predictions

BACKGROUND: Mathematical models can be used to predict the emergence and transmission of antiviral resistance. Previously it has been predicted that high usage of antivirals (in immunocompetent populations) to treat Herpes Simplex Virus type 2 (HSV-2) would only lead to fairly low levels of antiviral resistance. The HSV-2 predictions were based upon the assumption that drug-resistant strains of HSV-2 would be less infectious than drug-sensitive strains but that the drug-resistant strains would not be impaired in their ability to reactivate. Recent data suggest that some drug-resistant strains of HSV-2 are likely to be impaired in their ability to reactivate. Objectives: (1) To predict the effect of a high usage of antivirals on the prevalence of drug-resistant HSV-2 under the assumption that drug-resistant strains will be less infectious than drug-sensitive strains of HSV-2 and also have an impaired ability to reactivate. (2) To compare predictions with previous published predictions. METHODS: We generated theoretical drug-resistant HSV-2 strains that were attenuated (in comparison with drug-sensitive strains) in both infectivity and ability to reactivate. We then used a transmission model to predict the emergence and transmission of drug-resistant HSV-2 in the immunocompetent population assuming a high usage of antivirals. RESULTS: Our predictions are an order of magnitude lower than previous predictions; we predict that even after 25 years of high antiviral usage only 5 out of 10,000 immunocompetent individuals will be shedding drug-resistant virus. Furthermore, after 25 years, 52 cases of HSV-2 would have been prevented for each prevalent case of drug-resistant HSV-2. CONCLUSIONS: The predicted levels of drug-resistant HSV-2 for the immunocompetent population are so low that it seems unlikely that cases of drug-resistant HSV-2 will be detected

Temporal networks of face-to-face human interactions

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.

Envelope Determinants of Equine Lentiviral Vaccine Protection

Lentiviral envelope (Env) antigenic variation and associated immune evasion present major obstacles to vaccine development. The concept that Env is a critical determinant for vaccine efficacy is well accepted, however defined correlates of protection associated with Env variation have yet to be determined. We reported an attenuated equine infectious anemia virus (EIAV) vaccine study that directly examined the effect of lentiviral Env sequence variation on vaccine efficacy. The study identified a significant, inverse, linear correlation between vaccine efficacy and increasing divergence of the challenge virus Env gp90 protein compared to the vaccine virus gp90. The report demonstrated approximately 100% protection of immunized ponies from disease after challenge by virus with a homologous gp90 (EV0), and roughly 40% protection against challenge by virus (EV13) with a gp90 13% divergent from the vaccine strain. In the current study we examine whether the protection observed when challenging with the EV0 strain could be conferred to animals via chimeric challenge viruses between the EV0 and EV13 strains, allowing for mapping of protection to specific Env sequences. Viruses containing the EV13 proviral backbone and selected domains of the EV0 gp90 were constructed and in vitro and in vivo infectivity examined. Vaccine efficacy studies indicated that homology between the vaccine strain gp90 and the N-terminus of the challenge strain gp90 was capable of inducing immunity that resulted in significantly lower levels of post-challenge virus and significantly delayed the onset of disease. However, a homologous N-terminal region alone inserted in the EV13 backbone could not impart the 100% protection observed with the EV0 strain. Data presented here denote the complicated and potentially contradictory relationship between in vitro virulence and in vivo pathogenicity. The study highlights the importance of structural conformation for immunogens and emphasizes the need for antibody binding, not neutralizing, assays that correlate with vaccine protection. © 2013 Craigo et al