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The development of models for species' distributions is briefly reviewed, concentrating on logistic regression and discriminant analytical methods. Improvements in each type of modelling approach have led to increasingly accurate model predictions. This review addresses several key issues that now confront those wishing to choose the "right" sort of model for their own application. One major issue is the number of predictor variables to retain in the final model. Another is the problem of sparse datasets, or of data reported to administrative levels only, not to points. A third is the incorporation of spatial co-variance and auto-covariance in the modelling process. It is suggested that many of these problems can be resolved by adopting an information-theoretic approach whereby a group of reasonable potential models is specified in advance, and the "best" candidate model is selected among them. This approach of model selection and multi-model inference, using various derivatives of the Kullback-Leibler information or distance statistic, puts the biologist, with her or his insight, back in charge of the modelling process that is usually the domain of statisticians. Models are penalized when they contain too many variables; careful specification of the right set of candidate models may also be used to identify the importance of each predictor variable individually; and finally the degree to which the current "best" model improves on all the other models in the candidate set may be quantified. The ability definitely to exclude some models from the realm of all possible models appropriate for any particular distribution problem may be as important as the ability to identify the best current model.

Yellow fever has been subjected to partial control for decades, but there are signs that case numbers are now increasing globally, with the risk of local epidemic outbreaks. Dengue case numbers have also increased dramatically during the past 40 years and different serotypes have invaded new geographical areas. Despite the temporal changes in these closely related diseases, and their enormous public health impact, few attempts have been made to collect a comprehensive dataset of their spatial and temporal distributions. For this review, records of the occurrence of both diseases during the 20th century have been collected together and are used to define their climatic limits using remotely sensed satellite data within a discriminant analytical model framework. The resulting risk maps for these two diseases identify their different environmental requirements, and throw some light on their potential for co-occurrence in Africa and South East Asia.

This contribution documents the satellite data archives, data processing methods and temporal Fourier analysis (TFA) techniques used to create the remotely sensed datasets on the DVD distributed with this volume. The aim is to provide a detailed reference guide to the genesis of the data, rather than a standard review. These remotely sensed data cover the entire globe at either 1 x 1 or 8 x 8 km spatial resolution. We briefly evaluate the relationships between the 1 x 1 and 8 x 8 km global TFA products to explore their inter-compatibility. The 8 x 8 km TFA surfaces are used in the mapping procedures detailed in the subsequent disease mapping reviews, since the 1 x 1 km products have been validated less widely. Details are also provided on additional, current and planned sensors that should be able to provide continuity with these environmental variable surfaces, as well as other sources of global data that may be used for mapping infectious disease.

Evidence is presented that the evolution of the tick-borne flaviviruses is driven by biotic factors, principally the exploitation of new hosts as transmission routes. Because vector-borne diseases are limited by climatic conditions, however, abiotic factors have the potential to direct and constrain the evolutionary pathways. This idea is explored by testing the hypothesis that closely related viruses occupy more similar eco-climatic spaces than do more distantly related viruses. A statistical comparison of the conventional phylogenetic tree derived from molecular distances and a novel phenetic tree derived from distances between the climatic spaces within which each virus circulates, indicates that these trees match each other more closely than would be expected at random. This suggests that these viruses are indeed limited in the degree to which they can evolve into new environmental conditions.

Air, sea and land transport networks continue to expand in reach, speed of travel and volume of passengers and goods carried. Pathogens and their vectors can now move further, faster and in greater numbers than ever before. Three important consequences of global transport network expansion are infectious disease pandemics, vector invasion events and vector-borne pathogen importation. This review briefly examines some of the important historical examples of these disease and vector movements, such as the global influenza pandemics, the devastating Anopheles gambiae invasion of Brazil and the recent increases in imported Plasmodium falciparum malaria cases. We then outline potential approaches for future studies of disease movement, focussing on vector invasion and vector-borne disease importation. Such approaches allow us to explore the potential implications of international air travel, shipping routes and other methods of transport on global pathogen and vector traffic.

In this review we examine formally the conditions under which vector-borne diseases are likely to change, and the directions of those changes, under various scenarios of climate change. We specify the criteria that must be met in order to conclude that climate change is having an effect on vector-borne diseases. We then take several examples from the literature and show how some of them meet these criteria, while others do not. For those that do not, there are alternative explanations that involve much more plausible drivers of the recorded changes in the diseases concerned.

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

The human and animal trypanosomiases of Africa provide unique challenges to epidemiologists because of the spatial and temporal scales over which variation in transmission takes place. This chapter describes how our descriptions of the different components of transmission, from the parasites to the affected hosts, eventually developed to include geographical dimensions. It then briefly mentions two key analytical techniques used in the application of multi-temporal remotely sensed imagery to the interpretation of field data; temporal Fourier analysis for data reduction, and a variety of discriminant analytical techniques to describe the distribution and abundance of vectors and diseases. Satellite data may be used both for biological, process-based models and for statistical descriptions of vector populations and disease transmission. Examples are given of models for the tsetse Glossina morsitans in the Yankari Game Reserve, Nigeria, and in The Gambia. In both sites the satellite derived index of Land Surface Temperature (LST) is the best correlate of monthly mortality rates and is used to drive tsetse population models. The Gambia model is then supplemented with a disease transmission component; the mean infection rates of the vectors and of local cattle are satisfactorily described by the model, as are the seasonal variations of infection in the cattle. High and low spatial resolution satellite data have been used in a number of statistical studies of land cover types and tsetse habitats. In addition multi-temporal data may be related to both the incidence and prevalence of trypanosomiasis. Analysis of past and recent animal and human trypanosomiasis data from south-east Uganda supports the suggestion of the importance of cattle as a reservoir of the human disease in this area; mean infection prevalences in both human and animal hosts rise and fall in a similar fashion over the same range of increasing vegetation index values. Monthly sleeping sickness case data from the districts and counties of south-east Uganda are analysed and often show significant correlations with local LST. Case numbers increase with LST in areas that are relatively cooler than average for this part of Uganda, but decrease with LST in areas that are on average warmer. This indicates different seasonal cycles of risk across the region, and may be related to the differing vectorial roles of the two local tsetse, G. fuscipes and G. pallidipes. Finally, the increasing pace of change, and the likelihood of new or reemerging vector-borne diseases, highlight the need for accurate and timely information on habitat changes and the impacts these will have on disease transmission. The next generation of satellites will have significantly more spectral and spatial resolution than the current satellites, and will enable us to refine both statistical and biological predictions of trypanosomiasis and other vector-borne diseases within disease early warning systems.