Semi-Parametric Spatial Individual-level Disease Transmission Models

Rahul, Chinmoy Roy

Semi-Parametric Spatial Individual-level Disease Transmission Models

dc.contributor.advisor	Deardon, Rob
dc.contributor.author	Rahul, Chinmoy Roy
dc.contributor.committeemember	Tekougang, Thierry Chekouo
dc.contributor.committeemember	Kopciuk, Karen
dc.contributor.committeemember	Shen, Hua
dc.contributor.committeemember	Feng, Cindy
dc.date.accessioned	2024-08-01T15:29:37Z
dc.date.available	2024-08-01T15:29:37Z
dc.date.issued	2024-07-29
dc.description.abstract	Over recent years, there has been a noticeable increase in research activity on spatio-temporal statistical models to describe infectious disease dynamics. Individual-level models (ILMs), fitted in a Bayesian MCMC framework, can be used to understand the underlying mechanisms responsible for the spread of the infectious diseases, taking into account population heterogeneity via various individual-level covariates. There has also been a noticeable rise in the use of models that incorporate behavioral change dynamics. In either case, in modeling infectious disease spread parametric models are frequently employed, often depending on strong underlying assumptions regarding disease transmission mechanisms within the population. However, selecting appropriate parametric assumptions can be challenging in real-world scenarios, and incorrect assumptions may lead to erroneous conclusions. As an alternative, non-parametric approaches offer greater flexibility and robustness against strong assumptions. The aim of this study is to explore the use of semi-parametric spatial infectious disease transmission models in a Bayesian MCMC framework. This approach will help us to estimate the relationships between explanatory variables and the risk of infection with much more flexible assumptions compared to parametric approaches. To achieve our goal, we begin with considering ILMs that incorporate piecewise constant (step), or piecewise linear spatial functions, which may also have estimated change points. We also investigate the utilization of piecewise constant kernel spatial models for infectious disease transmission that integrate an ``alarm function" to account for population behavioral change (BC) resulting from increased infection prevalence over time. All models are fitted within a Bayesian MCMC framework. In this thesis, we explore results derived from both simulated and real-life epidemics, showing the greater flexibility of constant piecewise functions with and without BC effects, as well as piecewise linear spatial functions with both fixed and estimated change points. We also demonstrate the selection of the number of change points using the Deviance Information Criteria (DIC).
dc.identifier.citation	Rahul, C. R. (2024). Semi-parametric spatial individual-level disease transmission models (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/119278
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
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	Semi-parametric
dc.subject	Infectious disease modeling
dc.subject	Behavioural Change
dc.subject.classification	Biostatistics
dc.title	Semi-Parametric Spatial Individual-level Disease Transmission Models
dc.type	doctoral thesis
thesis.degree.discipline	Mathematics & Statistics
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
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.