Ensemble Model For Forecasting Infectious Diseases

Data Science for Infectious Disease Data Analytics: An Introduction with R provides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data. With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered coronavirus disease (COVID-19). The primary emphasis of this book is the data science procedures in epidemiological studies, including data wrangling, visualization, interpretation, predictive modeling, and inference, which is of immense importance due to increasingly diverse and nonexperimental data across a wide range of fields. The knowledge and skills readers gain from this book are also transferable to other areas, such as public health, business analytics, environmental studies, or spatio-temporal data visualization and analysis in general. Aimed at readers with an undergraduate knowledge of mathematics and statistics, this book is an ideal introduction to the development and implementation of data science in epidemiology. Features Describes the entire data science procedure of how the infectious disease data are collected, curated, visualized, and fed to predictive models, which facilitates effective communication between data sources, scientists, and decision-makers. Explains practical concepts of infectious disease data and provides particular data science perspectives. Overview of the unique features and issues of infectious disease data and how they impact epidemic modeling and projection. Introduces various classes of models and state-of-the-art learning methods to analyze infectious diseases data with valuable insights on how different models and methods could be connected.

Population Biology of Vector-Borne Diseases is the first comprehensive survey of this rapidly developing field. The chapter topics provide an up-to-date presentation of classical concepts, reviews of emerging trends, synthesis of existing knowledge, and a prospective agenda for future research. The contributions offer authoritative and international perspectives from leading thinkers in the field. The dynamics of vector-borne diseases are far more intrinsically ecological compared with their directly transmitted equivalents. The environmental dependence of ectotherm vectors means that vector-borne pathogens are acutely sensitive to changing environmental conditions. Although perennially important vector-borne diseases such as malaria and dengue have deeply informed our understanding of vector-borne diseases, recent emerging viruses such as West Nile virus, Chikungunya virus, and Zika virus have generated new scientific questions and practical problems. The study of vector-borne disease has been a particularly rich source of ecological questions, while ecological theory has provided the conceptual tools for thinking about their evolution, transmission, and spatial extent. Population Biology of Vector-Borne Diseases is an advanced textbook suitable for graduate level students taking courses in vector biology, population ecology, evolutionary ecology, disease ecology, medical entomology, viral ecology/evolution, and parasitology, as well as providing a key reference for researchers across these fields.

This book gathers high-quality papers presented at the Sixth International Conference on Smart Trends in Computing and Communications (SmartCom 2022), organized by Global Knowledge Research Foundation (GR Foundation) in partnership with IFIP InterYIT during January 11–12, 2022. It covers the state of the art and emerging topics in information, computer communications, and effective strategies for their use in engineering and managerial applications. It also explores and discusses the latest technological advances in, and future directions for, information and knowledge computing and its applications.

This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation. The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.

Provides an up-to-date, authoritative, and challenging review of the ecology and evolution of infectious diseases, focusing on low-income countries for effective public health applications and outcomes.

Mosquitoes transmit many of the pathogens that cause zoonotic diseases from wildlife and livestock to people, with devasting consequences for public health. The factors affecting the ecology and evolution of the transmission dynamics of these mosquito-borne pathogens can be revealed using multidisciplinary research approaches. This 7th volume of the ECVD series focuses on the ecological factors that determine the transmission dynamics of mosquito-borne pathogens naturally circulating between animals of different taxa and their importance for human health. The authors revise the current knowledge on the pathogens that affect wildlife, including those maintained in captivity, as well as the use of cutting-edge techniques for the identification of potential vectors of these pathogens. In addition, this volume explores the role of factors related to global change, including changes in landscape use, deforestation and urbanization, as major drivers of the distribution of mosquito vectors and the dynamics of pathogen transmission. Finally, updated information on the approaches used to identify and control mosquito-borne diseases is presented, with a particular focus on those affecting humans. In summary, this book provides an updated review of the different mosquito-borne pathogens affecting animals and their public health relevance.

Modeling and Inverse Problems in the Presence of Uncertainty collects recent research-including the authors' own substantial projects-on uncertainty propagation and quantification. It covers two sources of uncertainty: where uncertainty is present primarily due to measurement errors and where uncertainty is present due to the modeling formulation i