Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17520
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dc.contributor.advisorHENS, Niel-
dc.contributor.authorHouben, Kendra-
dc.date.accessioned2014-10-09T09:14:06Z-
dc.date.available2014-10-09T09:14:06Z-
dc.date.issued2014-
dc.identifier.urihttp://hdl.handle.net/1942/17520-
dc.description.abstractGoeyvaerts et al. (2010) used deterministic models to describe the seroprofile of PVB19. However, none of the models were able to fully capture the sero-epidemiology. Stochastic simulation models were developed because a stochastic process is more realistic. The SIR and SIRS models were made stochastic under several assumptions in an age homogeneous and age heterogeneous setting, with and without vital dynamics. The models in endemic equilibrium can be used to estimate the seroprofila of an infectious disease. In the future, the main goal is to fit a stochastic model to the data of PVB19.-
dc.format.mimetypeApplication/pdf-
dc.languageen-
dc.language.isoen-
dc.publishertUL-
dc.titleUsing stochastic simulation models to reconstruct B19 sero-epidemiology.-
dc.typeTheses and Dissertations-
local.format.pages0-
local.bibliographicCitation.jcatT2-
dc.description.notesMaster of Statistics-Epidemiology & Public Health Methodology-
local.type.specifiedMaster thesis-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.fullcitationHouben, Kendra (2014) Using stochastic simulation models to reconstruct B19 sero-epidemiology..-
Appears in Collections:Master theses
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