Modeling Disease Spread And Control

Mathematical models are useful tools to understand the epidemiology and agent-host interaction of diseases. They are developed and applied since over a century, but with increasing computer capacity, they become increasingly prominent as part of evidence based decision making. Mathematical models are frequently used to construct preparedness and contingency plans for highly contagious diseases such as foot-and-mouth disease. This allows proposing effective strategies to control the spread of the disease in case of an incursion, and avails useful tools to support decision making during an outbreak. They are also used to monitor, prevent and control endemic diseases within populations or farms. In addition, mathematical models improve our understanding of the contact structure between farms, pointing out risky elements in the contact network for disease introduction or further spread within the population. This Research Topic presents valuable studies presenting different aspects and implementations of mathematical modeling for disease spread and control in the veterinary field. The areas covered include model construction, network analysis, tools for decision makers, and costeffective control of endemic diseases.

Mathematical models are useful tools to understand the epidemiology and agent-host interaction of diseases. They are developed and applied since over a century, but with increasing computer capacity, they become increasingly prominent as part of evidence based decision making. Mathematical models are frequently used to construct preparedness and contingency plans for highly contagious diseases such as foot-and-mouth disease. This allows proposing effective strategies to control the spread of the disease in case of an incursion, and avails useful tools to support decision making during an outbreak. They are also used to monitor, prevent and control endemic diseases within populations or farms. In addition, mathematical models improve our understanding of the contact structure between farms, pointing out risky elements in the contact network for disease introduction or further spread within the population. This Research Topic presents valuable studies presenting different aspects and implementations of mathematical modeling for disease spread and control in the veterinary field. The areas covered include model construction, network analysis, tools for decision makers, and cost-effective control of endemic diseases.

The 1918-19 influenza epidemic killed more than fifty million people worldwide. The SARS epidemic of 2002-3, by comparison, killed fewer than a thousand. The success in containing the spread of SARS was due largely to the rapid global response of public health authorities, which was aided by insights resulting from mathematical models. Models enabled authorities to better understand how the disease spread and to assess the relative effectiveness of different control strategies. In this book, Lisa Sattenspiel and Alun Lloyd provide a comprehensive introduction to mathematical models in epidemiology and show how they can be used to predict and control the geographic spread of major infectious diseases. Key concepts in infectious disease modeling are explained, readers are guided from simple mathematical models to more complex ones, and the strengths and weaknesses of these models are explored. The book highlights the breadth of techniques available to modelers today, such as population-based and individual-based models, and covers specific applications as well. Sattenspiel and Lloyd examine the powerful mathematical models that health authorities have developed to understand the spatial distribution and geographic spread of influenza, measles, foot-and-mouth disease, and SARS. Analytic methods geographers use to study human infectious diseases and the dynamics of epidemics are also discussed. A must-read for students, researchers, and practitioners, no other book provides such an accessible introduction to this exciting and fast-evolving field.

Pathogenic microorganisms exploit a number of different routes for transmission and this book demonstrates how the spread of disease can be prevented through the practices of disinfection and controlling microbial growth. The book is organized into four sections.

Effectively Assess Intervention Options for Controlling Infectious DiseasesOur experiences with the human immunodeficiency virus (HIV), severe acute respiratory syndrome (SARS), and Ebola virus disease (EVD) remind us of the continuing need to be vigilant against the emergence of new infectious diseases. Mathematical modeling is increasingly used i

Mathematical models are increasingly used to guide public health policy decisions and explore questions in infectious disease control. Written for readers without advanced mathematical skills, this book provides an introduction to this area.

This volume summarizes the state-of-the-art in the fast growing research area of modeling the influence of information-driven human behavior on the spread and control of infectious diseases. In particular, it features the two main and inter-related “core” topics: behavioral changes in response to global threats, for example, pandemic influenza, and the pseudo-rational opposition to vaccines. In order to make realistic predictions, modelers need to go beyond classical mathematical epidemiology to take these dynamic effects into account. With contributions from experts in this field, the book fills a void in the literature. It goes beyond classical texts, yet preserves the rationale of many of them by sticking to the underlying biology without compromising on scientific rigor. Epidemiologists, theoretical biologists, biophysicists, applied mathematicians, and PhD students will benefit from this book. However, it is also written for Public Health professionals interested in understanding models, and to advanced undergraduate students, since it only requires a working knowledge of mathematical epidemiology.

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