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Title: Spatial scale, demography and the population dynamics of childhood diseases
Author: Green, C. J.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2003
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Childhood diseases remain an important public health issue, particularly in developing countries where they are still a significant cause of mortality. It is only through understanding the underlying mechanisms of such diseases that we can hope to control, and ultimately eradicate them. Here I attempt to address the epidemiological effects of spatial scale, from within-city through to pan-European dynamics. I also consider which factors are the major causes of the variabilities observed in the dynamics of childhood diseases, focusing on demography in particular. Questions of space (be they spatial scale or spatial heterogeneities) are of increasing interest in ecology, from individual interactions through to the population level and beyond to metapopulation studies. Space may also be of considerable importance in epidemiological studies, since disease prevention and control are highly dependent upon population size and the proximity to other susceptible individuals. This thesis presents a review of research in these areas (chapter one), analysis of within-city infection dynamics (in particular measles notifications in London boroughs, chapter two), and comparisons of these data with equivalent data from cities in England and Wales. These results are then compared with findings from both stochastic and deterministic models (chapter three). Chapter four introduces novel Scottish city data and employs a time-series SIR (susceptible, infectious, recovered) model to compare the various datasets. The study area is then widened still further and pan-European measles and whooping cough data are considered, with particular attention paid to the effects of variations in the underlying demographic parameters, alongside the potential for population dynamic interference between the two diseases (chapter five). These phenomena are then assessed with stochastic and deterministic models (chapter six). Chapter seven then unifies the data and the models, and suggests possible extensions of the model. Finally, chapter eight reviews the thesis and proposes possible avenues for future research.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available