This book highlights the estimate of epidemic characteristics for different countries/regions in the world with the use of known SIR (susceptible-infected-removed) model for the dynamics of the epidemic, the known exact solution of the linear differential equations and statistical approach developed before. The COVID-19 pandemic is of great interest to researchers due to its high mortality and a negative impact to the world economy. Correct simulation of the pandemic dynamics needs complicated mathematical models and many efforts for unknown parameters identification. The simple method of detection of the new pandemic wave is proposed and SIR model generalized. The hidden periods, epidemic durations, final numbers of cases, the effective reproduction numbers and probabilities of meeting an infected person are presented for countries like USA, Germany, UK, the Republic of Korea, Italy, Spain, France, the Republic of Moldova, Ukraine, and for the world. The presented information is useful to regulate the quarantine activities and to predict the medical and economic consequences of different/future pandemics.

This book depicts and reveals the socioeconomic dynamics of the COVID-19 crisis, and its global, regional, and local perspectives. Explicitly interdisciplinary, this volume embraces a wide spectrum of topics across economics, business, public management, psychology, and public health. Written by global experts, each chapter offers a snapshot of an emerging aspect of the COVID-19 crisis for the benefit of academics and students, as well as the institutional, economic, social, and developmental policymakers and health practitioners on the ground.

This book describes the spatial and temporal perspectives on COVID-19 and its impacts and deepens our understanding of human dynamics during and after the global pandemic. It critically examines the role smart city technologies play in shaping our lives in the years to come. The book covers a wide-range of issues related to conceptual, theoretical and data issues, analysis and modeling, and applications and policy implications such as socio-ecological perspectives, geospatial data ethics, mobility and migration during COVID-19, population health resilience and much more. With accelerated pace of technological advances and growing divide on political and policy options, a better understanding of disruptive global events such as COVID-19 with spatial and temporal perspectives is an imperative and will make the ultimate difference in public health and economic decision making. Through in-depth analyses of concepts, data, methods, and policies, this book stimulates future studies on global pandemics and their impacts on society at different levels.

Public health officials and organizations around the world remain on high alert because of increasing concerns about the prospect of an influenza pandemic, which many experts believe to be inevitable. Moreover, recent problems with the availability and strain-specificity of vaccine for annual flu epidemics in some countries and the rise of pandemic strains of avian flu in disparate geographic regions have alarmed experts about the world's ability to prevent or contain a human pandemic. The workshop summary, The Threat of Pandemic Influenza: Are We Ready? addresses these urgent concerns. The report describes what steps the United States and other countries have taken thus far to prepare for the next outbreak of "killer flu." It also looks at gaps in readiness, including hospitals' inability to absorb a surge of patients and many nations' incapacity to monitor and detect flu outbreaks. The report points to the need for international agreements to share flu vaccine and antiviral stockpiles to ensure that the 88 percent of nations that cannot manufacture or stockpile these products have access to them. It chronicles the toll of the H5N1 strain of avian flu currently circulating among poultry in many parts of Asia, which now accounts for the culling of millions of birds and the death of at least 50 persons. And it compares the costs of preparations with the costs of illness and death that could arise during an outbreak.

For epidemiologists, evolutionary biologists, and health-care professionals, real-time and predictive modeling of infectious disease is of growing importance. This book provides a timely and comprehensive introduction to the modeling of infectious diseases in humans and animals, focusing on recent developments as well as more traditional approaches. Matt Keeling and Pejman Rohani move from modeling with simple differential equations to more recent, complex models, where spatial structure, seasonal "forcing," or stochasticity influence the dynamics, and where computer simulation needs to be used to generate theory. In each of the eight chapters, they deal with a specific modeling approach or set of techniques designed to capture a particular biological factor. They illustrate the methodology used with examples from recent research literature on human and infectious disease modeling, showing how such techniques can be used in practice. Diseases considered include BSE, foot-and-mouth, HIV, measles, rubella, smallpox, and West Nile virus, among others. Particular attention is given throughout the book to the development of practical models, useful both as predictive tools and as a means to understand fundamental epidemiological processes. To emphasize this approach, the last chapter is dedicated to modeling and understanding the control of diseases through vaccination, quarantine, or culling. Comprehensive, practical introduction to infectious disease modeling Builds from simple to complex predictive models Models and methodology fully supported by examples drawn from research literature Practical models aid students' understanding of fundamental epidemiological processes For many of the models presented, the authors provide accompanying programs written in Java, C, Fortran, and MATLAB In-depth treatment of role of modeling in understanding disease control

Mobile forensics has grown from a relatively obscure tradecraft to a crucial part of many criminal investigations, and is now used daily by examiners and analysts within local, state, and federal law enforcement as well as within the military, US government organizations, and the private “e-Discovery” industry. Developments in forensic research, tools, and processes over the past decade have been very successful and continue to change at a rapid pace. Forensic Investigations and Risk Management in Mobile and Wireless Communications is a collection of innovative research on the methods and applications of analyzing mobile devices and data for collection of information pertaining to the legal evidence related to various security breaches and intrusion detection. While highlighting topics including cybercrime, neural networks, and smartphone security, this book is ideally designed for security analysts, IT professionals, researchers, practitioners, academicians, and students currently investigating the up-and-coming aspects surrounding network security, computer science, and security engineering.

Outbreaks of infectious diseases--such as Ebola, Zika, and pandemic viruses--have raised concerns from Congress about how federal agencies use modeling to, among other things, predict disease distribution and potential impacts. Chapter 1 discusses emerging infectious diseases, in light of the recent coronavirus outbreak, and the modeling tools used to detect, predict, and understand the spread of such diseases. Chapter 2 examines the extent to which HHS used models to inform policy, planning, and resource allocation for public health decisions; the extent to which HHS coordinated modeling efforts; steps HHS generally takes to assess model development and performance; and the extent to which HHS has addressed challenges related to modeling.

This book presents recently developed statistical methods and theory required for the application of the tools of functional data analysis to problems arising in geosciences, finance, economics and biology. It is concerned with inference based on second order statistics, especially those related to the functional principal component analysis. While it covers inference for independent and identically distributed functional data, its distinguishing feature is an in depth coverage of dependent functional data structures, including functional time series and spatially indexed functions. Specific inferential problems studied include two sample inference, change point analysis, tests for dependence in data and model residuals and functional prediction. All procedures are described algorithmically, illustrated on simulated and real data sets, and supported by a complete asymptotic theory. The book can be read at two levels. Readers interested primarily in methodology will find detailed descriptions of the methods and examples of their application. Researchers interested also in mathematical foundations will find carefully developed theory. The organization of the chapters makes it easy for the reader to choose an appropriate focus. The book introduces the requisite, and frequently used, Hilbert space formalism in a systematic manner. This will be useful to graduate or advanced undergraduate students seeking a self-contained introduction to the subject. Advanced researchers will find novel asymptotic arguments.

Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.