Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise. This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest Statistical Methods for Hospital Monitoring with R: Provides functions to perform quality improvement and infection management data analysis. Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events. Provides a summary of key non-statistical aspects of hospital safety and easy to use functions. Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.
This book uses the EM (expectation maximization) algorithm to simultaneously estimate the missing data and unknown parameter(s) associated with a data set. The parameters describe the component distributions of the mixture; the distributions may be continuous or discrete. The editors provide a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions along with MCMC computational methods, together with a range of detailed discussions covering the applications of the methods and features chapters from the leading experts on the subject. The applications are drawn from scientific discipline, including biostatistics, computer science, ecology and finance. This area of statistics is important to a range of disciplines, and its methodology attracts interest from researchers in the fields in which it can be applied.
This book is a source of information on practical and innovative approaches to biosecurity surveillance. It explains the foundation and concepts behind surveillance design, with examples of methods and tools created to deal with surveillance challenges. With supporting case studies and including current directions in research, it covers evidence-based approaches to surveillance, statistics, detectability, single and multi-species detection, risk assessment, diagnostics, data-basing, modelling of invasion and spread, optimisation, and future climate challenges.
This book will be the first to present the foundational issues of Bayesian modeling, finite and infinite mixture models, and MCMC computational methods, together with a range of detailed case studies covering the applications of the methods. The applications are drawn from a wide range of scientific disciplines, including biostatistics, ecology, bioinformatics, the social sciences and finance.
Agricultural trade is an engine for economic growth, yet many countries lack the competence and confidence to negotiate market access effectively. Access requires compliance with a set of phytosanitary measures imposed by the importing country. However, by following a structured process, negotiations can move beyond simple compliance to a more mutually beneficial solution. Beyond Compliance: A Production Chain Framework for Plant Health Risk Management in Trade provides a series of decision support tools that can be used to manage and demonstrate plant health risk management. The tools, developed within a production chain framework and Systems Approach, were developed using real trade cases in Southeast Asia. The project aimed to support national plant protection organisations and trade negotiators seeking to ensure safe trade with more risk-proportionate and suitable risk management plans. The Beyond Compliance project was funded by the Standards and Trade Development Facility, a global partnership established by the Food and Agriculture Organization of the United Nations, the World Bank, the World Health Organization, the World Organisation for Animal Health and the World Trade Organization.
Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise. This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest Statistical Methods for Hospital Monitoring with R: Provides functions to perform quality improvement and infection management data analysis. Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events. Provides a summary of key non-statistical aspects of hospital safety and easy to use functions. Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.
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