Document Server@UHasselt >
Education >
School for Information Technology >
Master theses >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17520

Title: Using stochastic simulation models to reconstruct B19 sero-epidemiology.
Authors: Houben, Kendra
Advisors: HENS, Niel
Issue Date: 2014
Publisher: tUL
Abstract: Goeyvaerts 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.
Notes: Master of Statistics-Epidemiology & Public Health Methodology
URI: http://hdl.handle.net/1942/17520
Category: T2
Type: Theses and Dissertations
Appears in Collections: Master theses

Files in This Item:

Description SizeFormat
N/A399.02 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.