Genomic epidemiology of methicillin-resistant and -susceptible Staphylococcus aureus from bloodstream infections

Background Bloodstream infections due to Staphylococcus aureus cause significant patient morbidity and mortality worldwide. Of major concern is the emergence and spread of methicillin-resistant S. aureus (MRSA) in bloodstream infections, which are associated with therapeutic failure and increased mortality. Methods We generated high quality draft genomes from 323 S. aureus blood culture isolates from patients diagnosed with bloodstream infection at the Dartmouth-Hitchcock Medical Center, New Hampshire, USA in 2010–2018. Results In silico detection of antimicrobial resistance genes revealed that 133/323 isolates (41.18%) carry horizontally acquired genes conferring resistance to at least three antimicrobial classes, with resistance determinants for aminoglycosides, beta-lactams and macrolides being the most prevalent. The most common resistance genes were blaZ and mecA, which were found in 262/323 (81.11%) and 104/323 (32.20%) isolates, respectively. Majority of the MRSA (102/105 isolates or 97.14%) identified using in vitro screening were related to two clonal complexes (CC) 5 and 8. The two CCs emerged in the New Hampshire population at separate times. We estimated that the time to the most recent common ancestor of CC5 was 1973 (95% highest posterior density (HPD) intervals: 1966–1979) and 1946 for CC8 (95% HPD intervals: 1924–1959). The effective population size of CC8 increased until the late 1960s when it started to level off until late 2000s. The levelling off of CC8 in 1968 coincided with the acquisition of SCCmec Type IV in majority of the strains. The plateau in CC8 also coincided with the acceleration in the population growth of CC5 carrying SCCmec Type II in the early 1970s, which eventually leveled off in the early 1990s. Lastly, we found evidence for frequent recombination in the two clones during their recent clonal expansion, which has likely contributed to their success in the population. Conclusions We conclude that the S. aureus population was shaped mainly by the clonal expansion, recombination and co-dominance of two major MRSA clones in the last five decades in New Hampshire, USA. These results have important implications on the development of effective and robust strategies for intervention, control and treatment of life-threatening bloodstream infections.

Reliability estimation in a multicomponent stress–strength based on unit-Gompertz distribution

Purpose The purpose of this paper is to estimate the multicomponent reliability by assuming the unit-Gompertz (UG) distribution. Both stress and strength are assumed to have an UG distribution with common scale parameter. Design/methodology/approach The reliability of a multicomponent stress–strength system is obtained by the maximum likelihood (MLE) and Bayesian method of estimation. Bayes estimates of system reliability are obtained by using Lindley’s approximation and Metropolis–Hastings (M–H) algorithm methods when all the parameters are unknown. The highest posterior density credible interval is obtained by using M–H algorithm method. Besides, uniformly minimum variance unbiased estimator and exact Bayes estimates of system reliability have been obtained when the common scale parameter is known and the results are compared for both small and large samples. Findings Based on the simulation results, the authors observe that Bayes method provides better estimation results as compared to MLE. Proposed asymptotic and HPD intervals show satisfactory coverage probabilities. However, average length of HPD intervals tends to remain shorter than the corresponding asymptotic interval. Overall the authors have observed that better estimates of the reliability may be achieved when the common scale parameter is known. Originality/value Most of the lifetime distributions used in reliability analysis, such as exponential, Lindley, gamma, lognormal, Weibull and Chen, only exhibit constant, monotonically increasing, decreasing and bathtub-shaped hazard rates. However, in many applications in reliability and survival analysis, the most realistic hazard rates are upside-down bathtub and bathtub-shaped, which are found in the unit-Gompertz distribution. Furthermore, when reliability is measured as percentage or ratio, it is important to have models defined on the unit interval in order to have plausible results. Therefore, the authors have studied the multicomponent stress–strength reliability under the unit-Gompertz distribution by comparing the MLEs, Bayes estimators and UMVUEs.

Bayesian Estimation of Stress Strength Reliability using Upper Record Values from Generalised Inverted Exponential Distribution

The paper develops Bayesian estimators and HPD intervals for the stress strength reliability of generalised inverted exponential distribution using upper record values. For prior distribution, informative prior as well as non-informative prior both are considered. The Bayes estimators are obtained under both symmetric and asymmetric loss functions. A simulation study is conducted to obtain the Bayes estimates of stress strength reliability. Simulated data sets are also considered here for illustration purpose.

Bayesian Inference on the Shape Parameter and Future Observation of Exponentiated Family of Distributions

The Bayes estimators of the shape parameter of exponentiated family of distributions have been derived by considering extension of Jeffreys' noninformative as well as conjugate priors under different scale-invariant loss functions, namely, weighted quadratic loss function, squared-log error loss function and general entropy loss function. The risk functions of these estimators have been studied. We have also considered the highest posterior density (HPD) intervals for the parameter and the equal-tail and HPD prediction intervals for future observation. Finally, we analyze one data set for illustration.

Computation of posterior distribution in Bayesian analysis – application in an intermittently used reliability system

Bayesian estimation is presented for the stationary rate of disappointments, D∞, for two models (with different specifications) of intermittently used systems. The random variables in the system are considered to be independently exponentially distributed. Jeffreys’ prior is assumed for the unknown parameters in the system. Inference about D∞ is being restrained in both models by the complex and non-linear definition of D∞. Monte Carlo simulation is used to derive the posterior distribution of D∞ and subsequently the highest posterior density (HPD) intervals. A numerical example where Bayes estimates and the HPD intervals are determined illustrates these results. This illustration is extended to determine the frequentistical properties of this Bayes procedure, by calculating covering proportions for each of these HPD intervals, assuming fixed values for the parameters.