Building A Platform For Data Driven Pandemic Prediction
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Building a Platform for Data-Driven Pandemic Prediction
This book is about building platforms for pandemic prediction. It provides an overview of probabilistic prediction for pandemic modeling based on a data-driven approach. It also provides guidance on building platforms with currently available technology using tools such as R, Shiny, and interactive plotting programs. The focus is on the integration of statistics and computing tools rather than on an in-depth analysis of all possibilities on each side. Readers can follow different reading paths through the book, depending on their needs. The book is meant as a basis for further investigation of statistical modelling, implementation tools, monitoring aspects, and software functionalities. Features: A general but parsimonious class of models to perform statistical prediction for epidemics, using a Bayesian approach Implementation of automated routines to obtain daily prediction results How to interactively visualize the model results Strategies for monitoring the performance of the predictions and identifying potential issues in the results Discusses the many decisions required to develop and publish online platforms Supplemented by an R package and its specific functionalities to model epidemic outbreaks The book is geared towards practitioners with an interest in the development and presentation of results in an online platform of statistical analysis of epidemiological data. The primary audience includes applied statisticians, biostatisticians, computer scientists, epidemiologists, and professionals interested in learning more about epidemic modelling in general, including the COVID-19 pandemic, and platform building. The authors are professors at the Statistics Department at Universidade Federal de Minas Gerais. Their research records exhibit contributions applied to a number of areas of Science, including Epidemiology. Their research activities include books published with Chapman and Hall/CRC and papers in high quality journals. They have also been involved with academic management of graduate programs in Statistics and one of them is currently the President of the Brazilian Statistical Association.
Building a Platform for Data-Driven Pandemic Prediction
This book is about building platforms for pandemic prediction. It provides an overview of probabilistic prediction for pandemic modeling based on a data-driven approach. It also provides guidance on building platforms with currently available technology using tools such as R, Shiny, and interactive plotting programs. The focus is on the integration of statistics and computing tools rather than on an in-depth analysis of all possibilities on each side. Readers can follow different reading paths through the book, depending on their needs. The book is meant as a basis for further investigation of statistical modelling, implementation tools, monitoring aspects, and software functionalities. Features: A general but parsimonious class of models to perform statistical prediction for epidemics, using a Bayesian approach Implementation of automated routines to obtain daily prediction results How to interactively visualize the model results Strategies for monitoring the performance of the predictions and identifying potential issues in the results Discusses the many decisions required to develop and publish online platforms Supplemented by an R package and its specific functionalities to model epidemic outbreaks The book is geared towards practitioners with an interest in the development and presentation of results in an online platform of statistical analysis of epidemiological data. The primary audience includes applied statisticians, biostatisticians, computer scientists, epidemiologists, and professionals interested in learning more about epidemic modelling in general, including the COVID-19 pandemic, and platform building. The authors are professors at the Statistics Department at Universidade Federal de Minas Gerais. Their research records exhibit contributions applied to a number of areas of Science, including Epidemiology. Their research activities include books published with Chapman and Hall/CRC and papers in high quality journals. They have also been involved with academic management of graduate programs in Statistics and one of them is currently the President of the Brazilian Statistical Association.
Data-Driven Decision Support System in Intelligent HealthCare
Machine Intelligence with Generative AI is one of the most trending topics with applications in almost all fields of life. In healthcare, it is not only accelerating the development of new products, but also automating the generation of new and synthetic content making it easier to train and improve machine learning models. Some of the biggest achievements of Generative AI in healthcare have been drug discovery, personalized care, differentially private synthetic data generation, operational efficiency, and many more. Generative AI models like Generative Adversarial Networks, and Variational Autoencoders are employed to generate synthetic medical images, aiding in data augmentation, facilitating disease diagnosis, and enabling advanced medical imaging research. Additionally, Generative AI techniques are being utilized for creating realistic electronic health records (EHRs) and simulated patient data, supporting privacy-preserving data sharing, and empowering innovative studies for personalized medicine and drug development. NLP models like ClinicalBERT use transformer-based deep learning architecture to understand and represent contextual information in large clinical text datasets, such as electronic health records (EHRs) and medical literature, and can better grasp medical terminologies, domain-specific language, and contextual nuances that are unique to the healthcare field. This volume delves into the realm of Machine Intelligence with Generative AI and explores its impact on the healthcare industry.