Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

The increasing use of virtual reality (VR) simulators in surgical training makes it imperative that definitive studies be performed to assess their training effectiveness. Indeed, in this paper we report the meta-analysis of the efficacy of virtual reality simulators in: 1) the transference of skills from the simulator training environment to the operating room, and 2) their ability to discriminate between the experience levels of their users. The task completion time and the error score were the two study outcomes collated and analyzed in this meta-analysis. Sixteen studies were identified from a computer-based literature search (1996-2004). The meta-analysis of the random effects model (because of the heterogeneity of the data) revealed that training on virtual reality simulators did lessen the time taken to complete a given surgical task as well as clearly differentiate between the experienced and the novice trainees. Meta-analytic studies such as the one reported here would be very helpful in the planning and setting up of surgical training programs and for the establishment of reference 'learning curves' for a specific simulator and surgical task. If any such programs already exist, they can then indicate the improvements to be made in the simulator used, such as providing for more variety in their case scenarios based on the state and/or rate of learning of the trainee.

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We investigate the growth of a class of networks in which a new node first picks a mediator at random and connects with m randomly chosen neighbors of the mediator at each time step. We show that degree distribution in such a mediation-driven attachment (MDA) network exhibits power-law P (k) ∼ k −γ(m) with a spectrum of exponents depending on m. To appreciate the contrast between MDA and Barabási-Albert (BA) networks, we then discuss their rank-size distribution. To quantify how long a leader, the node with the maximum degree, persists in its leadership as the network evolves, we investigate the leadership persistence probability F (τ ) i.e. the probability that a leader retains its leadership up to time τ . We find that it exhibits a power-law F (τ ) ∼ τ −θ(m) with persistence exponent θ(m) ≈ 1.51 ∀ m in the MDA networks and θ(m) → 1.53 exponentially with m in the BA networks.