Data Subset-Based Methods of Inference for Spatial Individual Level Epidemic Models
Date
2023-08
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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.
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Keywords
Bayesian inference, Individual leve model
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.