Nicole Bohme Carnegie

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In this work, we estimate the proportions of transmissions occurring in main vs. casual partnerships, and by the sexual role, infection stage, and testing and treatment history of the infected partner, for men who have sex with men (MSM) in the US and Peru. We use dynamic, stochastic models based in exponential random graph models (ERGMs), obtaining inputs(More)
There has been a great deal of interest recently in the modeling and simulation of dynamic networks, i.e., networks that change over time. One promising model is the separable temporal exponential-family random graph model (ERGM) of Krivitsky and Handcock, which treats the formation and dissolution of ties in parallel at each time step as independent ERGMs.(More)
BACKGROUND Generalized heterosexual epidemics are responsible for the largest share of the global burden of HIV. These occur in populations that do not have high rates of partner acquisition, and research suggests that a pattern of fewer, but concurrent, partnerships may be the mechanism that provides the connectivity necessary for sustained transmission.(More)
BACKGROUND Three trials have demonstrated the prophylactic effect of male circumcision (MC) for HIV acquisition among heterosexuals, and MC interventions are underway throughout sub-Saharan Africa. Similar efforts for men who have sex with men (MSM) are stymied by the potential for circumcised MSM to acquire HIV easily through receptive sex and transmit(More)
BACKGROUND We aim to identify optimal strategies for deploying pre-exposure prophylaxis among men who have sex with men (MSM) in the United States and Peru to maximize population-level effectiveness in an efficient manner. We use epidemic models to simulate the impact of targeting strategies. Most studies have focused on targeting either the general(More)
Linkage analysis is useful in investigating disease transmission dynamics and the effect of interventions on them, but estimates of probabilities of linkage between infected people from observed data can be biased downward when missingness is informative. We investigate variation in the rates at which subjects' viral genotypes link across groups defined by(More)
The incidence of new infections is a key measure of the status of the HIV epidemic, but accurate measurement of incidence is often constrained by limited data. Karon et al. (Statist. Med. 2008; 27:4617–4633) developed a model to estimate the incidence of HIV infection from surveillance data with biologic testing for recent infection for newly diagnosed(More)
When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis strategy(More)
Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network(More)