dc.contributor.author	Allotey, Clifford
dc.contributor.examiningcommittee	Arino, Julien (Mathematics) Collera, Juancho (Mathematics and Computer Science, University of the Philippines Baguio)	en_US
dc.contributor.supervisor	Magpantay, Felicia (Mathematics)	en_US
dc.date.accessioned	2017-09-18T19:47:53Z
dc.date.available	2017-09-18T19:47:53Z
dc.date.issued	2017
dc.degree.discipline	Mathematics	en_US
dc.degree.level	Master of Science (M.Sc.)	en_US
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.description.note	February 2018	en_US
dc.identifier.uri	http://hdl.handle.net/1993/32615
dc.language.iso	eng	en_US
dc.rights	open access	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.type	master thesis	en_US

A comparison of existing measles models

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