Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537230
Title: Spatio-temporal dynamics of the 1918 influenza pandemic
Author: Eggo, Rosalind M.
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2011
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Abstract:
The 1918 influenza pandemic was one of the most devastating and enigmatic infectious disease outbreaks in history. I have applied epidemiological methods to datasets collected at the time to contribute to the understanding of the spread of novel pathogens. The first part of this thesis addresses the wave structure of the pandemic in the UK. House-to-house surveys after the pandemic in seven cities in England collected statistics on the number of people reporting symptoms in each wave, and the frequency of multiple reports. These data were reanalysed using dynamic models to explore the relationships between reported infection in one wave and in subsequent waves. These survey data were combined with weekly mortality counts to allow more than one source to inform on the output of the model. I reported how cross immunity from one wave to another may impact on the wave structure and investigated how assumptions about probability of reporting affect the results. Potential mechanisms for repeated infection and their implications are discussed. The second part of this thesis examines the spatial spread of the major wave of the pandemic in England and Wales and the United States. I used mortality data to determine the onset of the autumn epidemic in each city, and parameterised a gravity model to explain the spread of influenza from city-to-city. I fitted the model to the onset times as a space-time survival process and determined the parameters of the model by Bayesian inference for each dataset. I found that for England and Wales, where the dataset has high coverage, the density dependence in interactions between cities is important for correctly describing and reproducing the spread of the epidemic. For the US where the dataset is sparse and contains only large cities, this parameter is not required to reproduce the spatial pattern of disease spread.
Supervisor: Ferguson, Neil ; Cauchemez, Simon Sponsor: Medical Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.537230  DOI: Not available
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