Data Subset-Based Methods of Inference for Spatial Individual Level Epidemic Models

Nyein, Thet Htet Chan

Data Subset-Based Methods of Inference for Spatial Individual Level Epidemic Models

dc.contributor.advisor	Deardon, Rob
dc.contributor.author	Nyein, Thet Htet Chan
dc.contributor.committeemember	Shen, Hua
dc.contributor.committeemember	Kopciuk, Karen A.
dc.date	2023-11
dc.date.accessioned	2023-08-17T19:26:48Z
dc.date.available	2023-08-17T19:26:48Z
dc.date.issued	2023-08
dc.description.abstract	Mathematical models are essential to understand infectious disease dynamics, enabling to control the spread of those diseases and preparing for public health measures. Since time and space are important factors affecting the transmission of infectious diseases, spatial individual-level models (ILM) with both temporal and spatial information are developed. Typically, Markov Chain Monte Carlo (MCMC) methods are utilized for the inference of ILM. Nonetheless, this approach can be computationally intensive for complex or large models, resulting in repeated likelihood calculations. This thesis explores various spatial and temporal subset methods to conduct statistical inference for spatial epidemic models, aiming to provide appropriate parameter estimates with minimum computational resources. In this thesis, we utilize the spatial ILM with the Euclidean distance between susceptible individuals and infectious individuals as a kernel function.
dc.identifier.citation	Nyein, T. H. C. (2023). Data subset-based methods of inference for spatial individual level epidemic models (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/116867
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/41709
dc.language.iso	en
dc.publisher.faculty	Science
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Bayesian inference
dc.subject	Individual leve model
dc.subject.classification	Statistics
dc.title	Data Subset-Based Methods of Inference for Spatial Individual Level Epidemic Models
dc.type	master thesis
thesis.degree.discipline	Mathematics & Statistics
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.