https://www.selleckchem.com/products/unc-3230.html © The Author(s) 2020. Published by Oxford University Press.Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1,653) and Middle Tennessee, USA (n = 2,779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates,k not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth. © The Author(s) 2020. Published by Oxford University Press.Background Although dietary modification is strongly recommended for prevention and treatment of hypertension, little is known about which factors are associated with adherence to dietary guidelines. We investigated knowledge and attitude, perceived benefits of, barriers to, and self-efficacy of dietary therapy, and identified the factors associated with dietary adherence among adults with and without hypertension. Methods We collected information on the knowledge/attitudes and perceived benefits of dietary therapy, as well as barriers to and self-efficacy regarding dietary adherence from