scholarly journals Current levels of gonorrhoea screening in MSM in Belgium may have little effect on prevalence: a modelling study

2018 ◽  
Vol 146 (3) ◽  
pp. 333-338 ◽  
Author(s):  
J. Buyze ◽  
W. Vanden Berghe ◽  
N. Hens ◽  
C. Kenyon
Keyword(s):  
Exponential Family ◽  
R Package ◽  
Sexual Relationships ◽  
Antibiotics Resistance ◽  
Graph Models ◽  
Considerable Uncertainty ◽  
Random Graph Models ◽  
Increased Risk ◽  
Sex With Men ◽  
Day By Day

AbstractThere is considerable uncertainty as to the effectiveness of Neisseria gonorrhoeae (NG) screening in men who have sex with men. It is important to ensure that screening has benefits that outweigh the risks of increased antibiotics resistance. We develop a mathematical model to estimate the effectiveness of screening on prevalence. Separable Temporal Exponential family Random Graph Models are used to model the sexual relationships network, both with main and casual partners. Next, the transmission of Gonorrhoea is simulated on this network. The models are implemented using the R package ‘statnet’, which we adapted among other things to incorporate infection status at the pharynx, urethra and rectum separately and to distinguish between anal sex, oral sex and rimming. The different screening programmes compared are no screening, 3.5% of the population screened, 32% screened and 50% screened. The model simulates day-by-day evolution for 10 years of a population of 10 000. If half of the population would be screened, the prevalence in the pharynx decreases from 11.9% to 10.2%. We conclude that the limited impact of screening on NG prevalence may not outweigh the increased risk of antibiotic resistance.

scholarly journals Exponential-family Random Graph Models for Rank-order Relational Data

2017 ◽  
Vol 47 (1) ◽  
pp. 68-112 ◽  
Author(s):  
Pavel N. Krivitsky ◽  
Carter T. Butts
Keyword(s):  
General Framework ◽  
Exponential Family ◽  
Rank Order ◽  
Relative Volume ◽  
Relational Data ◽  
Interpersonal Interaction ◽  
Graph Models ◽  
Cross Sectional ◽  
Sufficient Statistics ◽  
Random Graph Models

Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of interpersonal interaction among members of a technology education program.

scholarly journals Exponential-Family Random Graph Models for Multi-Layer Networks

Psychometrika ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. 630-659
Author(s):  
Pavel N. Krivitsky ◽  
Laura M. Koehly ◽  
Christopher Steven Marcum
Keyword(s):  
Random Graph ◽  
Exponential Family ◽  
Graph Models ◽  
Random Graph Models

scholarly journals Exponential-Family Random Graph Models for Multi-Layer Networks

2019 ◽  
Author(s):  
Pavel N Krivitsky ◽  
Laura Koehly ◽  
Christopher Steven Marcum
Keyword(s):  
Random Graph ◽  
Binomial Distribution ◽  
Exponential Family ◽  
Published Data ◽  
Data Sets ◽  
Graph Models ◽  
Random Graph Models ◽  
Special Cases

Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway--Maxwell-Binomial distribution to model the marginal dependence among multiple layers; a "layer logic" language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and non-degenerate triadic and degree effects. The developments are demonstrated on two previously published data sets.

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