Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever

2017 ◽  
Author(s):  
Julio Albinati ◽  
Wagner Meira ◽  
Gisele L. Pappa ◽  
Andrew G. Wilson
Keyword(s):  
Gaussian Process ◽  
Dengue Fever ◽  
Spatio Temporal

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

Spatio-temporal patterns of dengue fever cases in Kaoshiung City, Taiwan, 2003–2008

2012 ◽  
Vol 34 ◽  
pp. 587-594 ◽  
Author(s):  
Ya-Hui Hsueh ◽  
Jay Lee ◽  
Lisa Beltz
Keyword(s):  
Dengue Fever ◽  
Temporal Patterns ◽  
Spatio Temporal

Prediction of Abdominal Aortic Aneurysms Using Sparse Gaussian Process Regression

2013 ◽  
Author(s):  
A. Ijaz ◽  
J. Choi ◽  
W. Lee ◽  
S. Baek
Keyword(s):  
Gaussian Process ◽  
Morphological Changes ◽  
Gaussian Process Regression ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Abdominal Aortic ◽  
Risk Of Rupture ◽  
Spatial Estimates ◽  
Spatio Temporal ◽  
Small Aneurysms

Abdominal Aortic Aneurysms (AAA) is a form of vascular disease causing focal enlargement of abdominal aorta. It affects a large part of population and has up to 90% mortality rate. Since risks from open surgery or endovascular repair outweighs the risk of AAA rupture, surgical treatments are not recommended with AAA less than 5.5cm in diameter. Recent clinical recommendations suggest that people with small aneurysms should be examined 3∼36 months depending on size to get information about morphological changes. While advances in biomechanics provide state-of-the-art spatial estimates of stress distributions of AAA, there are still limitations in modeling its time evolution. Thus, there is no biomechanical framework to utilize such information from a series of medical images that would aid physicians in detecting small aneurysms with high risk of rupture. For the present study, we use series of CT images of small AAAs taken at different times to model and predict the spatio-temporal evolution of AAA. This is achieved using sparse local Gaussian process regression.

scholarly journals Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors

2018 ◽  
Vol 66 (17) ◽  
pp. 4598-4611 ◽  
Author(s):  
Danil Kuzin ◽  
Olga Isupova ◽  
Lyudmila Mihaylova
Keyword(s):  
Gaussian Process ◽  
Sparse Regression ◽  
Spatio Temporal

scholarly journals Exploitation of Kronecker Structure in Gaussian Process Regression for Efficient Biomedical Signal Processing

2021 ◽  
Vol 7 (2) ◽  
pp. 287-290
Author(s):  
Jannik Prüßmann ◽  
Jan Graßhoff ◽  
Philipp Rostalski
Keyword(s):  
Gaussian Process ◽  
Source Separation ◽  
Gaussian Process Regression ◽  
Efficient Solutions ◽  
Biomedical Signal Processing ◽  
Hyperparameter Optimization ◽  
Performance Loss ◽  
Versatile Tool ◽  
Data Points ◽  
Spatio Temporal

Abstract Gaussian processes are a versatile tool for data processing. Unfortunately, due to storage and runtime requirements, standard Gaussian process (GP) methods are limited to a few thousand data points. Thus, they are infeasible in most biomedical, spatio-temporal problems. The methods treated in this work cover GP inference and hyperparameter optimization, exploiting the Kronecker structure of covariance matrices. To solve regression and source separation problems, two different approaches are presented. The first approach uses efficient matrix-vector-products, whilst the second approach is based on efficient solutions to the eigendecomposition. The latter also enables efficient hyperparameter optimization. In comparison to standard GP methods, the proposed methods can be applied to very large biomedical datasets without any further performance loss and perform substantially faster. The performance is demonstrated on esophageal manometry data, where the cardiac and respiratory signal components are to be inferred by source separation.

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