BEAST 2: a software platform for Bayesian evolutionary analysis.

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.

Patient satisfaction in 21st century medicine: revolution or evolution?

The Moment-of-Truth (MOT) patient satisfaction system was created to address each patient's medical care and service needs at the "point-of-care," before the patient leaves the medical facility. The MOT system is patient-centered by actively involving each patient in his or her own healthcare evaluation, planning, and continuous quality improvement. Patient needs are aligned with the required healthcare resources, which simultaneously produce information that can be acted upon "immediately," at the point-of-care, with "a sense of urgency"-addressing patient expectations each and every time the patient encounters the healthcare system. Major changes that occurred in medical service delivery at Hudson Hospital after implementation of the MOT system included a change in the focus of healthcare delivery toward the patient each and every time medical care or service occurred by placing the patient at the center of the care continuum; the ability to capture and react to what the patient needed at the place and time the patient needed it; and the incorporation of patient satisfaction as a way of doing business, throughout the healthcare organization. Results in 2007 to date have averaged 98% among responding patients indicating that they would recommend the Hudson Hospital to family and friends.

Inferring epidemiological dynamics with Bayesian coalescent inference: the merits of deterministic and stochastic models.

Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingman's coalescent theory. Here, we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent susceptible-infected-removed (SIR) tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the performance of the two coalescent models and also juxtapose results obtained with a recently published birth-death-sampling model for epidemic inference. Comparisons are made by analyzing sets of genealogies simulated under precisely known epidemiological parameters. Additionally, we analyze influenza A (H1N1) sequence data sampled in the Canterbury region of New Zealand and HIV-1 sequence data obtained from known United Kingdom infection clusters. We show that both coalescent SIR models are effective at estimating epidemiological parameters from data with large fundamental reproductive number [Formula: see text] and large population size [Formula: see text]. Furthermore, we find that the stochastic variant generally outperforms its deterministic counterpart in terms of error, bias, and highest posterior density coverage, particularly for smaller [Formula: see text] and [Formula: see text]. However, each of these inference models is shown to have undesirable properties in certain circumstances, especially for epidemic outbreaks with [Formula: see text] close to one or with small effective susceptible populations.