Comparison of three methods for ascertainment of contact information relevant to respiratory pathogen transmission in encounter networks

<p>Abstract</p> <p>Background</p> <p>Mathematical models of infection that consider targeted interventions are exquisitely dependent on the assumed mixing patterns of the population. We report on a pilot study designed to assess three different methods (one retrospective, two prospective) for obtaining contact data relevant to the determination of these mixing patterns.</p> <p>Methods</p> <p>65 adults were asked to record their social encounters in each location visited during 6 study days using a novel method whereby a change in physical location of the study participant triggered data entry. Using a cross-over design, all participants recorded encounters on 3 days in a paper diary and 3 days using an electronic recording device (PDA). Participants were randomised to first prospective recording method.</p> <p>Results</p> <p>Both methods captured more contacts than a pre-study questionnaire, but ascertainment using the paper diary was superior to the PDA (mean difference: 4.52 (95% CI 0.28, 8.77). Paper diaries were found more acceptable to the participants compared with the PDA. Statistical analysis confirms that our results are broadly consistent with those reported from large-scale European based surveys. An association between household size (trend 0.14, 95% CI (0.06, 0.22), <it>P </it>< 0.001) and composition (presence of child 0.37, 95% CI (0.17, 0.56), <it>P </it>< 0.001) and the total number of reported contacts was observed, highlighting the importance of sampling study populations based on household characteristics as well as age. New contacts were still being recorded on the third study day, but compliance had declined, indicating that the optimal number of sample days represents a trade-off between completeness and quality of data for an individual.</p> <p>Conclusions</p> <p>The study's location-based reporting design allows greater scope compared to other methods for examining differences in the characteristics of encounters over a range of environments. Improved parameterisation of dynamic transmission models gained from work of this type will aid in the development of more robust decision support tools to assist health policy makers and planners.</p

Influence of Contact Definitions in Assessment of the Relative Importance of Social Settings in Disease Transmission Risk

BACKGROUND: Realistic models of disease transmission incorporating complex population heterogeneities require input from quantitative population mixing studies. We use contact diaries to assess the relative importance of social settings in respiratory pathogen spread using three measures of person contact hours (PCH) as proxies for transmission risk with an aim to inform bipartite network models of respiratory pathogen transmission. METHODS AND FINDINGS: Our survey examines the contact behaviour for a convenience sample of 65 adults, with each encounter classified as occurring in a work, retail, home, social, travel or "other" setting. The diary design allows for extraction of PCH-interaction (cumulative time in face-face conversational or touch interaction with contacts)--analogous to the contact measure used in several existing surveys--as well as PCH-setting (product of time spent in setting and number of people present) and PCH-reach (product of time spent in setting and number of people in close proximity). Heterogeneities in day-dependent distribution of risk across settings are analysed using partitioning and cluster analyses and compared between days and contact measures. Although home is typically the highest-risk setting when PCH measures isolate two-way interactions, its relative importance compared to social and work settings may reduce when adopting a more inclusive contact measure that considers the number and duration of potential exposure events. CONCLUSIONS: Heterogeneities in location-dependent contact behaviour as measured by contact diary studies depend on the adopted contact definition. We find that contact measures isolating face-face conversational or touch interactions suggest that contact in the home dominates, whereas more inclusive contact measures indicate that home and work settings may be of higher importance. In the absence of definitive knowledge of the contact required to facilitate transmission of various respiratory pathogens, it is important for surveys to consider alternative contact measures

Credibility in Policy Expertise: The Function of Boundaries Between Research and Policy

As science becomes an increasingly crucial resource for addressing complex challenges in society, extensive demands are placed upon the researchers who produce it. Creating valuable expert knowledge that intervenes in policy or practice requires knowledge brokers to facilitate interactions at the boundary between research and policy. Yet, existing research lacks a compelling account of the ways in which brokerage is performed to gain credibility. Drawing on mixed-method analysis of twelve policy research settings, I outline a novel set of strategies for attaining symbolic power, whereby policy experts position themselves and others via conceptual distances drawn between the ‘world of ideas’ and the ‘world of policy and practice’. Disciplinary distance works to situate research as either disciplinary or undisciplinary, epistemic distance creates a boundary between complex specialist research and direct digestible outputs, temporal distance represents the separation of slow rigorous research and agile responsive analysis, and economic distance situates research as either pure and intrinsic or marketable and fundable. I develop a theoretical account that unpacks the boundaries between research communities and shows how these boundaries permit policy research actors to achieve various strategic aims.ESRC Future Research Leaders ES/N016319/1 Commonwealth Scholarship Commissio

Exponential random graph model specifications for bipartite networks: a dependence hierarchy

In this paper, we review the development of dependence structures for exponential random graph models for bipartite networks, and propose a hierarchy of dependence structures within which different dependence assumptions may be located. Based on this hierarchy, we propose a new set of model specifications by including bipartite graph configurations involving more than four nodes. We discuss the theoretical significance of the various effects that the extended models afford, and illustrate application of this hierarchy of models to several bipartite networks related to the political mobilization in Brazil in the early 1990s (Mische, 2007)

Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation

Missing data are often problematic in social network analysis since what is missing may potentially alter the conclusions about what we have observed as tie-variables need to be interpreted in relation to their local neighbourhood and the global structure. Some ad hoc methods for dealing with missing data in social networks have been proposed but here we consider a model-based approach. We discuss various aspects of fitting exponential family random graph (or p-star) models (ERGMs) to networks with missing data and present a Bayesian data augmentation algorithm for the purpose of estimation. This involves drawing from the full conditional posterior distribution of the parameters, something which is made possible by recently developed algorithms. With ERGMs already having complicated interdependencies, it is particularly important to provide inference that adequately describes the uncertainty, something that the Bayesian approach provides. To the extent that we wish to explore the missing parts of the network, the posterior predictive distributions, immediately available at the termination of the algorithm, are at our disposal, which allows us to explore the distribution of what is missing unconditionally on any particular parameter values. Some important features of treating missing data and of the implementation of the algorithm are illustrated using a well-known collaboration network and a variety of missing data scenarios

Closure, connectivity and degrees: new specifications for exponential random graph (p*) models for directed social networks

The new higher order specifications for exponential random graph models introduced by Snijders, Pattison, Robins & Handcock (2006) exhibit dramatic improvements in model fit compared with the commonly used Markov random graphs. Snijders et al briefly presented versions of these new specifications for directed graphs, in particular a directed alternating ktriangle parameter, based on closure of multiple two-paths. In this paper, we present a number of additional higher order parameters for directed graphs. Most importantly, we propose three new triadic-based parameters to represent different versions of triadic closure: cyclic effects; transitivity based on shared choices of partners; and transitivity based on shared popularity. We also introduce corresponding parameters for multiple connectivity effects. We propose some fifty graph features to be investigated in goodness of fit diagnostics for these new parameters. As empirical illustrations, we develop models for two sets of organizational network data, to show that the new parameters help with an optimal representation of the data. The first example is a trust network within a training group, and the second a 'work difficulty' network within a government instrumentality. In the first example we show that our additional parameters are necessary to obtain an acceptable model for the data. The second example is novel in fitting a statistical model, and inferring structural processes, for a negative tie network. Using this second example, we show how the incorporation of additional effects - the number of sources and sinks in the network, and the correlation between the in- and out-degree distributions - can improve representation of the degree distribution. The final model acceptably replicates the negative tie network in terms of: statistics related to twenty different graph configurations; the in- and out-degree distribution, including their correlation; seven different graph clustering coefficients; the triad census; and the geodesic distribution. Model interpretation emphasizes the importance of some nodes receiving high numbers of negative ties