networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling

The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in estimating the null distribution of graph statistics. This paper describes the networksis package for R and how its simulate and simulate_sis functions can be used to address both of these tasks as well as generate initial graphs for Markov chain Monte Carlo simulations

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling

The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in estimating the null distribution of graph statistics. This paper describes the networksis package for R and how its simulate and simulate_sis functions can be used to address both of these tasks as well as generate initial graphs for Markov chain Monte Carlo simulation

Modeling concurrency and selective mixing in heterosexual partnership networks with applications to sexually transmitted diseases

Network-based models for sexually transmitted disease transmission rely on initial partnership networks incorporating structures that may be related to risk of infection. In particular, initial networks should reflect the level of concurrency and attribute-based selective mixing observed in the population of interest. We consider momentary degree distributions as measures of concurrency and propensities for people of certain types to form partnerships with each other as a measure of attribute-based selective mixing. Estimation of momentary degree distributions and mixing patterns typically relies on cross-sectional survey data, and, in the context of heterosexual networks, we describe how this results in two sets of reports that need not be consistent with each other. The reported momentary degree distributions and mixing totals are related through a series of constraints, however. We provide a method to incorporate those in jointly estimating momentary degree distributions and mixing totals. We develop a method to simulate heterosexual networks consistent with these momentary degree distributions and mixing totals, applying it to data obtained from the National Longitudinal Study of Adolescent Health. We first use the momentary degree distributions and mixing totals as mean value parameters to estimate the natural parameters for an exponential-family random graph model and then use a Markov chain Monte Carlo algorithm to simulate person-level heterosexual partnership networks

Population constraints on pooled surveys in demographic hazard modeling.

In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women's education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size

Evolution of a wild-plant tobamovirus passaged through an exotic host: Fixation of mutations and increased replication

Tobamovirus is a group of viruses that have become serious pathogens of crop plants. As part of a study informing risk of wild plant virus spill over to crops, we investigated the capacity of a solanaceous-infecting tobamovirus from an isolated indigenous flora to adapt to new exotic hosts. Yellow tailflower mild mottle virus (YTMMV) (genus Tobamovirus, family Virgaviridae) was isolated from a wild plant of yellow tailflower (Anthocercis littoria, family Solanaceae) and initially passaged through a plant of Nicotiana benthamiana, then one of Nicotiana glutinosa where a single local lesion was used to inoculate a N. benthamiana plant. Sap from this plant was used as starting material for nine serial passages through three plant species. The virus titre was recorded periodically, and 85% of the virus genome was sequenced at each passage for each host. Six polymorphic sites were found in the YTMMV genome across all hosts and passages. At five of these, the alternate alleles became fixed in the viral genome until the end of the experiment. Of these five alleles, one was a non-synonymous mutation (U1499C) that occurred only when the virus replicated in tomato. The mutant isolate harbouring U1499C, designated YTMMV-d, increased its titre over passages in tomato and outcompeted the wild-type isolate when both were co-inoculated to tomato. That YTMMV-d had greater reproductive fitness in an exotic host than did the wild type isolate suggests YTMMV evolution is influenced by host changes