Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/28081
Title: Evaluation of real-time methods for epidemic forecasting
Authors: LEE HUEY CHYI
Keywords: epidemic forecasting, influenza, importance sampling, particle filter
Issue Date: 12-Sep-2011
Source: LEE HUEY CHYI (2011-09-12). Evaluation of real-time methods for epidemic forecasting. ScholarBank@NUS Repository.
Abstract: This study evaluates different models and methods for epidemic forecasting with real-time data. We propose the Richards model, and two compartmental SIR models--one deterministic, the other stochastic--under a Bayesian simulation-based inferential framework to model the 2009 influenza A-H1N1 pandemic. Real-time data collection was based on reporting of cases of influenza-like-illnesses by doctors in a network of general practice/family doctor clinics we established in Singapore in the weeks before community transmission became widespread, as we have previously reported. The approaches used to derive estimates of the posterior distribution of epidemic model parameters are Markov chain Monte Carlo methods, importance sampling and particle filtering. We assess the predictive performance of the three models quantitatively by using several performance metrics. The effects of informative and non-informative priors on the predictions are also assessed. Our conclusion is that stochastic SIR model with particle filter is the most effective among our models and can be applied together with a real-time surveillance system to deliver predictions for future pandemic outbreaks. We also conclude that deterministic SIR model performs well but requires more computational time, whereas the non-compartmental Richards model is unable to predict effectively in advance of the peak.
URI: http://scholarbank.nus.edu.sg/handle/10635/28081
Appears in Collections:Master's Theses (Open)

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