| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Bootsma, Martin | |
| dc.contributor.author | Bouman, Bram | |
| dc.date.accessioned | 2025-08-12T15:00:41Z | |
| dc.date.available | 2025-08-12T15:00:41Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/49692 | |
| dc.description.abstract | Contact tracing is a method that was used for the COVID-19 pandemic to reduce the
transmission of the infectious disease. However, the actual impact of this measure is
hard to quantify. In this paper we analyze a SIQR model, which overestimates the
contact tracing rate. Furthermore, we create a stochastic model based on the Gillespie
algorithm to analyze how accurate this overestimation is. Both models assume
one-step contact tracing. We found a 37.5% reduction in peak total infection of the
SIQR with CT model compared to the standard SIR model. The stochastic model has
a 7.7% higher total infection peak, which can be attributed to the fact that the contact
tracing rate is overestimated. | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | In this thesis we estimate a contact tracing term for a SIR model. This term is later compared to a different stochastic model based on the Gillespie algorithm. | |
| dc.title | Contact tracing for infectious disease models on complete graphs | |
| dc.type.content | Bachelor Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | Contact tracing; SIR model; Gillespie algorithm | |
| dc.subject.courseuu | Wiskunde & Toepassingen | |
| dc.thesis.id | 51255 | |
