Imperial College London
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ProfessorSamirBhatt

Faculty of Medicine, School of Public Health

Professor of Statistics and Public Health
 
 
 
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Contact

 

+44 (0)20 7594 5029s.bhatt

 
 
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Location

 

G32ASt Mary's Research BuildingSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Monod:2023:10.1214/22-BA1334,
author = {Monod, M and Blenkinsop, A and Brizzi, A and Chen, Y and Cardoso, Correia Perello C and Jogarah, V and Wang, Y and Flaxman, S and Bhatt, S and Ratmann, O},
doi = {10.1214/22-BA1334},
journal = {Bayesian Analysis},
pages = {957--987},
title = {Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths},
url = {http://dx.doi.org/10.1214/22-BA1334},
volume = {18},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach over time and age, a low-rank Gaussian Process (GP) projected by regularised B-splines. We show that this projection defines a new GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the B-splines projected GP may perform better than standard B-splines and Bayesian P-splines, and equivalently well as a standard GP at considerably lowerruntimes. We apply the model to weekly, age-stratified COVID-19 attributabledeaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mortality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that vaccination rates were significantly associated with the magnitude of resurgences in COVID-19 deaths during the summer 2021. With counterfactual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The B-splines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesianregularising priors.
AU  - Monod,M
AU  - Blenkinsop,A
AU  - Brizzi,A
AU  - Chen,Y
AU  - Cardoso,Correia Perello C
AU  - Jogarah,V
AU  - Wang,Y
AU  - Flaxman,S
AU  - Bhatt,S
AU  - Ratmann,O
DO  - 10.1214/22-BA1334
EP  - 987
PY  - 2023///
SN  - 1931-6690
SP  - 957
TI  - Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths
T2  - Bayesian Analysis
UR  - http://dx.doi.org/10.1214/22-BA1334
UR  - http://hdl.handle.net/10044/1/99544
VL  - 18
ER  -
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