The last half century has witnessed two landmark events in medical history. The 1970s saw euphoria about the defeat of one of humankind's oldest disease scourges with the global eradication of smallpox. To set against this, the 2020s are experiencing the pandemic ravages of new viral diseases, of which COVID-19 is currently the most potent. But it is only the latest of a succession of threats. A Geography of Infection explores the distinctive spatial patterns and processes by which such infectious diseases spread from place to place and can grow from local and regional epidemics into global pandemics. This resource focuses initially on the local scale of doctors' practices and small islands where epidemic outbreaks are slight in the numbers infected and in geographical extent. Such local area studies raise two questions. First, how and where do epidemic diseases emerge and second, why do more diseases appear to be emerging now? To approach such questions implies a shift in spatial gear from painting epidemics with a fine-tipped local brush to an expanded palette on which doctors' practices and small islands are replaced by regional and global populations. Simultaneously, time bands are extended backwards to the origins of civilization and forwards into the twenty-first century. It eventually leads to a consideration of global pandemics - both historical (for example, plague, cholera and influenza) and contemporary (HIV/AIDS and COVID-19) and examines the ways the spread of infection can be prevented. All chapters are extensively illustrated with full-colour diagrams and maps - some of which are in colour for the first time. Bringing together the authors' collective 150 years of experience in research, mapping, and writing on spatial aspects of medical history, this is an invaluable resource for anyone interested in the spread, control, and eradication of epidemic and pandemic diseases.

Elements of Spatial Structure is a contribution to the literature on spatial series. Written by a group with varied backgrounds in engineering, geography and statistics, who collaborated at Bristol University in the early 1970s, the book analyses certain basic properties of spatial structure. It shows how spatial elements form an essential part of the real-world problems of population distribution, of urban and regional economies, and of diffusion processes. The book is divided into three parts. Part one is concerned with a static cross-sectional approach to spatial structure and explores some aspects of the central geographical problems of region-building and trend-surface mapping. Part two moves to a dynamic framework and extends time-series analysis in a regional framework. It includes two chapters on the application of the models presented to diffusion and unemployment data in south-west England. Part three links the first two parts together through a consideration of spatial autocorrelation and spatial forecasting. Again, formal models are presented and an attempt is made to gauge their utility by applying them to test problems in the south-west.

Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.