Spatio-temporal models in epidemiology and climate change
Guide(s)
Deb, Soudeep
Department
Decision Sciences
Area
Decision Sciences
University
Indian Institute of Management Bangalore
Place
Bangalore
Publication Date
3-31-2024
Year Awarded
March 2024
Year Completed
March 2024
Year Registered
June 2019
Abstract
In this thesis, we apply spatio-temporal models to three different problems related to climate change and epidemiology. The first problem discusses the application of the spatio-temporal model to understand the spread of COVID-19 in the USA. The proposed model uses a separable Gaussian spatio-temporal process in conjunction with an additive mean structure and a random error process. The model is implemented through a Bayesian framework, thereby providing a computational advantage over the classical way. We use state-level data from the United States of America in this study. For the second problem, we study identifying changepoints for spatio-temporal ordered categorical data. This is a novel methodology applied to COVID-19 data to get interesting insights regarding the "waves" of COVID-19. The model leverages an additive mean structure with separable Gaussian space-time processes. Our proposed technique is defined in such a way that it can detect a shift in the mean structure and the covariance structures in both the spatial and temporal associations. Our approach's capability to handle ordinal categorical data provides an added advantage from an application perspective. For the third problem, we turn our attention to climate change. We propose a new spatio-temporal technique to analyze the effects of rising temperatures on the rainfall dynamics of Bangladesh. Our model is defined through an additive mean function incorporating a space-time interaction effect, two spatiotemporally dependent processes, and a white noise structure. On the one hand, this specification helps account for possible differences in the trend functions and the spatio-temporal dependence patterns for different regions, while on the other, it allows for spatial non-stationarity and a general non-separable framework.
Pagination
ix, 122p.
Copyright
Indian Institute of Management Bangalore
Document Type
Dissertation
DAC Chairperson
Deb, Soudeep
DAC Members
Das, Shubhabrata; Shah, Arpit
Type of Degree
Ph.D.
Recommended Citation
Rawat, Siddharth, "Spatio-temporal models in epidemiology and climate change" (2024). Doctoral Dissertations. 18.
https://research.iimb.ac.in/doc_dissertations/18
Relation
DIS-IIMB-FPM-P24-18
