Since the start of the COVID-19 pandemic in December 2019, much research has
been done to develop the spatial-temporal methods to track it and to predict the
spread of the virus. In this thesis, a COVID-19 dataset containing the number of biweekly infected cases registered in Ontario since the start of the pandemic to the end
of June 2021 is analysed using Bayesian Spatial-temporal models and Area-to-area
(Area-to-point) Poisson Kriging models. With the Bayesian models, spatial-temporal
effects on infected risk will be checked and ATP Poisson Kriging models will show
how the virus spreads over the space and the spatial clustering feature. According
to these models, a Shinyapp website https://mujingrui.shinyapps.io/covid19 is
developed to present the results.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/43218 |
| Date | 25 January 2022 |
| Creators | Mu, Jingrui |
| Contributors | Alvo, Mayer |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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