Semi-Parametric Spatial Individual-level Disease Transmission Models
Date
2024-07-29
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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).
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Keywords
Semi-parametric, Infectious disease modeling, Behavioural Change
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.