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dc.contributor.supervisor Magpantay, Felicia (Mathematics) en_US
dc.contributor.author Allotey, Clifford
dc.date.accessioned 2017-09-18T19:47:53Z
dc.date.available 2017-09-18T19:47:53Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/1993/32615
dc.description.abstract With improvements in computational power, more and more mechanistic models are being formulated to explain physical and biological phenomena. In this thesis, we assess the strengths and weaknesses of different well-known models of pre-vaccine era measles dynamics. We investigate the assumptions inherent in each model, how well they fit to pre-vaccine era data and how well simple extensions of them perform when extended to modeling vaccine era dynamics. The four focus models we studied were (1) the standard deterministic SEIR models with school-term forcing, (2) the stochastic SEIR model with school-term forcing, (3) the Bjornstad et al. [2002] time series SIR model with a general time-varying transmission rate and (4) the He et al. [2010] model which incorporates school-term forcing, cohort effect and infection from outside the population into the model. We found that the He et al. [2010] model, fitted using maximum likelihood estimation, is the best model in terms of likelihood and the Akaike information criterion. However, this model was also the lowest ranked among all four models when comparing fit using residuals, leading to some open questions on possible trade offs between noise and likelihood. en_US
dc.subject Epidemiology en_US
dc.subject Mathematics en_US
dc.subject Measles models en_US
dc.title A comparison of existing measles models en_US
dc.degree.discipline Mathematics en_US
dc.contributor.examiningcommittee Arino, Julien (Mathematics) Collera, Juancho (Mathematics and Computer Science, University of the Philippines Baguio) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note February 2018 en_US


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