Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24624
Appears in Collections:Computing Science and Mathematics eTheses
Title: Prediction of Infectious Disease outbreaks based on limited information
Authors: Marmara, Vincent Anthony
Supervisor(s): Kleczkowski, Adam
Keywords: Epidemiology
SEIR Model
Reproduction number
Influenza Forecasting
Early warning modelling
Influenza survey
Epidemiological modelling
Parameter estimation
Issue Date: Sep-2016
Publisher: University of Stirling
Citation: V. Marmara, A. Cook, A. Kleczkowski, Estimation of force of infection based on different epidemiological proxies: 2009/2010 Influenza epidemic in Malta, Epidemics, 9 (2014) 52-61.
Abstract: The last two decades have seen several large-scale epidemics of international impact, including human, animal and plant epidemics. Policy makers face health challenges that require epidemic predictions based on limited information. There is therefore a pressing need to construct models that allow us to frame all available information to predict an emerging outbreak and to control it in a timely manner. The aim of this thesis is to develop an early-warning modelling approach that can predict emerging disease outbreaks. Based on Bayesian techniques ideally suited to combine information from different sources into a single modelling and estimation framework, I developed a suite of approaches to epidemiological data that can deal with data from different sources and of varying quality. The SEIR model, particle filter algorithm and a number of influenza-related datasets were utilised to examine various models and methodologies to predict influenza outbreaks. The data included a combination of consultations and diagnosed influenza-like illness (ILI) cases for five influenza seasons. I showed that for the pandemic season, different proxies lead to similar behaviour of the effective reproduction number. For influenza datasets, there exists a strong relationship between consultations and diagnosed datasets, especially when considering time-dependent models. Individual parameters for different influenza seasons provided similar values, thereby offering an opportunity to utilise such information in future outbreaks. Moreover, my findings showed that when the temperature drops below 14°C, this triggers the first substantial rise in the number of ILI cases, highlighting that temperature data is an important signal to trigger the start of the influenza epidemic. Further probing was carried out among Maltese citizens and estimates on the under-reporting rate of the seasonal influenza were established. Based on these findings, a new epidemiological model and framework were developed, providing accurate real-time forecasts with a clear early warning signal to the influenza outbreak. This research utilised a combination of novel data sources to predict influenza outbreaks. Such information is beneficial for health authorities to plan health strategies and control epidemics.
Type: Thesis or Dissertation
URI: http://hdl.handle.net/1893/24624

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