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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
Masks have been a ubiquitous and oft-politicized aspect of the COVID-19 pandemic. Years of painstakingly organized pre-pandemic planning documents led public health experts to initially discourage the use of masks, or even insinuate that they could lead to increased rates of spread. Yet seemingly in a matter of days in spring 2020, leading infectious disease scientists and organizations reversed their previous positions and recommended masking as the key tool to slow the spread of COVID and dramatically reduce infections. Unmasked tells the story of how effective or ineffective masks and mask mandate policies were in impacting the trajectory of the pandemic throughout the world. Author Ian Miller covers the earliest days of the pandemic, from experts such as Dr. Anthony Fauci contradicting their previous statements and recommending masks as the most important policy intervention against the spread of COVID, to the months afterward as many locations around the globe mandated masks in nearly all public settings. With easy-to-understand charts and visual aids, along with detailed, clear explanations of the dramatic shift in policy and expectations, Unmasked makes the data-driven case that masks might not have achieved the goals that Fauci and other public health experts created.
Any strategy to cope with an influenza pandemic must be based on the knowledge and tools that are available at the time an epidemic may occur. In the near term, when we lack an adequate supply of vaccine and antiviral medication, strategies that rely on social distancing and physical barriers will be relatively more prominent as means to prevent spread of disease. The use of respirators and facemasks is one key part of a larger strategy to establish barriers and increase distance between infected and uninfected individuals. Respirators and facemasks may have a role in both clinical care and community settings. Reusability of Facemasks During an Influenza Pandemic: Facing the Flu answers a specific question about the role of respirators and facemasks to reduce the spread of flu: Can respirators and facemasks that are designed to be disposable be reused safely and effectively? The committee-assisted by outstanding staff-worked intensively to review the pertinent literature; consult with manufacturers, researchers, and medical specialists; and apply their expert judgment. This report offers findings and recommendations based on the evidence, pointing to actions that are appropriate now and to lines of research that can better inform future decisions.
When communities face complex public health emergencies, state local, tribal, and territorial public health agencies must make difficult decisions regarding how to effectively respond. The public health emergency preparedness and response (PHEPR) system, with its multifaceted mission to prevent, protect against, quickly respond to, and recover from public health emergencies, is inherently complex and encompasses policies, organizations, and programs. Since the events of September 11, 2001, the United States has invested billions of dollars and immeasurable amounts of human capital to develop and enhance public health emergency preparedness and infrastructure to respond to a wide range of public health threats, including infectious diseases, natural disasters, and chemical, biological, radiological, and nuclear events. Despite the investments in research and the growing body of empirical literature on a range of preparedness and response capabilities and functions, there has been no national-level, comprehensive review and grading of evidence for public health emergency preparedness and response practices comparable to those utilized in medicine and other public health fields. Evidence-Based Practice for Public Health Emergency Preparedness and Response reviews the state of the evidence on PHEPR practices and the improvements necessary to move the field forward and to strengthen the PHEPR system. This publication evaluates PHEPR evidence to understand the balance of benefits and harms of PHEPR practices, with a focus on four main areas of PHEPR: engagement with and training of community-based partners to improve the outcomes of at-risk populations after public health emergencies; activation of a public health emergency operations center; communication of public health alerts and guidance to technical audiences during a public health emergency; and implementation of quarantine to reduce the spread of contagious illness.
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Procedures to identify mathematical models explicitly yielding optimal (maximum accuracy) solutions for samples were widely studied in the past century, with literatures emerging in fields such as symbolic logic, operations research, mathematical programming, systems engineering, algorithms, computer science, machine intelligence, finance, transportation science, and management science. Broad-spectrum consensus among disparate experts indicates predictive accuracy is an objective function worthy of optimization. In the Optimal (?optimizing?) Data Analysis (ODA) statistical paradigm, an optimization algorithm is first utilized to identify the model that explicitly maximizes predictive accuracy for the sample, and then the resulting optimal performance is evaluated in the context of an application-specific exact statistical architecture. Discovered in 1990, the most basic ODA model was a distribution-free machine learning algorithm used to make maximum accuracy classifications of observations into one of two categories (pass or fail) on the basis of their score on an ordered attribute (test score). When the first book on ODA was written in 2004 a cornucopia of indisputable evidence had already amassed demonstrating that statistical models identified by ODA were more flexible, transparent, intuitive, accurate, parsimonious, and generalizable than competing models instead identified using an unintegrated menagerie of legacy statistical methods. Understanding of ODA methodology skyrocketed over the next decade, and 2014 produced the development of novometric theory ? the conceptual analogue of quantum mechanics for the statistical analysis of classical data. This point was selected to pause to write Maximizing Predictive Accuracy, as a means of organizing and making sense of all that has so-far been learned about ODA, through November of 2015.Researchers exploring ODA for the first time will appreciate the intellectually transparent, intuitive presentation involving minimal use of a few simple equations. Researchers using ODA in their work will appreciate the unmatched flexibility, simplicity, and accuracy of resulting statistical models ? and their generalizability across time and sample. ODA accommodates all metrics, requires no distributional assumptions, allows for analytic weighting of individual observations, explicitly maximizes predictive accuracy (overall, or normed against chance), and supports multiple methods of assessing validity.
Racial and ethnic disparities in health care are known to reflect access to care and other issues that arise from differing socioeconomic conditions. There is, however, increasing evidence that even after such differences are accounted for, race and ethnicity remain significant predictors of the quality of health care received. In Unequal Treatment, a panel of experts documents this evidence and explores how persons of color experience the health care environment. The book examines how disparities in treatment may arise in health care systems and looks at aspects of the clinical encounter that may contribute to such disparities. Patients' and providers' attitudes, expectations, and behavior are analyzed. How to intervene? Unequal Treatment offers recommendations for improvements in medical care financing, allocation of care, availability of language translation, community-based care, and other arenas. The committee highlights the potential of cross-cultural education to improve provider-patient communication and offers a detailed look at how to integrate cross-cultural learning within the health professions. The book concludes with recommendations for data collection and research initiatives. Unequal Treatment will be vitally important to health care policymakers, administrators, providers, educators, and students as well as advocates for people of color.
An old man walks along deserted railway tracks, long since unused and overgrown; beside him a young, feral boy helps him along. It has been 60 years since the great Red Death wiped out mankind, and the handful of survivors from all walks of life have established their own civilization and their own hierarchy in a savage world. Art, science, and all learning has been lost, and the young descendants of the healthy know nothing of the world that was—nothing but myths and make-believe. The old man is the only one who can convey the wonders of that bygone age, and the horrors of the plague that brought about its end. What future lies in store for the remnants of mankind can only be surmised—their ignorance, barbarity, and ruthlessness the only hopes they have. This cataclysmic tale remains a terrifying prophecy of the perils of globalization, which are all too pertinent today.
Optimal Data Analysis: A Guidebook With Software for Windows offers the only statistical analysis paradigm that maximizes (weighted) predictive accuracy. This unique book fully explains this paradigm and includes simple-to-use software that empowers a universe of associated analyses. For any specific sample and exploratory or confirmatory hypothesis, optimal data analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability.
The ultimate guide for anyone wondering how President Joe Biden will respond to the COVID-19 pandemic—all his plans, goals, and executive orders in response to the coronavirus crisis. Shortly after being inaugurated as the 46th President of the United States, Joe Biden and his administration released this 200 page guide detailing his plans to respond to the coronavirus pandemic. The National Strategy for the COVID-19 Response and Pandemic Preparedness breaks down seven crucial goals of President Joe Biden's administration with regards to the coronavirus pandemic: 1. Restore trust with the American people. 2. Mount a safe, effective, and comprehensive vaccination campaign. 3. Mitigate spread through expanding masking, testing, data, treatments, health care workforce, and clear public health standards. 4. Immediately expand emergency relief and exercise the Defense Production Act. 5. Safely reopen schools, businesses, and travel while protecting workers. 6. Protect those most at risk and advance equity, including across racial, ethnic and rural/urban lines. 7. Restore U.S. leadership globally and build better preparedness for future threats. Each of these goals are explained and detailed in the book, with evidence about the current circumstances and how we got here, as well as plans and concrete steps to achieve each goal. Also included is the full text of the many Executive Orders that will be issued by President Biden to achieve each of these goals. The National Strategy for the COVID-19 Response and Pandemic Preparedness is required reading for anyone interested in or concerned about the COVID-19 pandemic and its effects on American society.