Grant Number:	2R01CA095994-05A1 Interpret this number
Primary Investigator:	Wakefield, Jonathan
Organization:	University Of Washington
Project Title:	Spatio-Temporal Epidemiology: Methods and Applications
Fiscal Year:	2014

DESCRIPTION (provided by applicant): A major public health aim is to provide accurate estimates and predictions of disease burden at the area level; this endeavor is vital for prevention strategies. This proposal describes methods for modeling spatially and temporally indexed health data. The first two aims concern infectious disease data and the third aim small-area estimation (SAE) based on complex survey data. Despite the widespread collection of infectious disease data in time and space there are important gaps in statistical methodology for the analysis of such data. Specifically, there are both a limited number of modeling options and a lack of software implementations for those that are available. Hence, the emphasis in this project is on practically applicable methods that will be made available within freely-available software, based on modern Bayesian smoothing models. With respect to infectious disease data, a flexible spline- based model is proposed. The methods will be developed in the context of a number of developing world infectious disease applications including hand, foot and mouth disease and tuberculosis. For SAE the proposed approach combines design-based estimation techniques with spatial smoothing priors, to produce estimates with both low bias and low variance. These models will be applied to data from the Behavioral Risk Factor Surveillance System (BRFSS) and to infant mortality and HIV data from the Tanzania demographic and health survey (DHS), in order to answer important public health questions.




A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts.
Authors: Fintzi J. , Wakefield J. , Minin V.N. .
Source: Biometrics, 2021-08-09 00:00:00.0; , .
EPub date: 2021-08-09 00:00:00.0.
PMID: 34374071
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Harmonizing child mortality data at disparate geographic levels.
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Source: Statistical Methods In Medical Research, 2021 May; 30(5), p. 1187-1210.
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Space-time modeling of child mortality at the Admin-2 level in a low and middle income countries context.
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Child mortality estimation incorporating summary birth history data.
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Pointless spatial modeling.
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Source: Biostatistics (oxford, England), 2020-04-01 00:00:00.0; 21(2), p. e17-e32.
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Trends in Sociodemographic Disparities in Colorectal Cancer Staging and Survival: A SEER-Medicare Analysis.
Authors: Liang P.S. , Mayer J.D. , Wakefield J. , Trinh-Shevrin C. , Kwon S.C. , Sherman S.E. , Ko C.W. .
Source: Clinical And Translational Gastroenterology, 2020 03; 11(3), p. e00155.
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Ecological inference for infectious disease data, with application to vaccination strategies.
Authors: Fisher L.H. , Wakefield J. .
Source: Statistics In Medicine, 2019-12-03 00:00:00.0; , .
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Changes in the spatial distribution of the under-five mortality rate: Small-area analysis of 122 DHS surveys in 262 subregions of 35 countries in Africa.
Authors: Li Z. , Hsiao Y. , Godwin J. , Martin B.D. , Wakefield J. , Clark S.J. , with support from the United Nations Inter-agency Group for Child Mortality Estimation and its technical advisory group .
Source: Plos One, 2019; 14(1), p. e0210645.
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Associations between neighbourhood characteristics and depression: a twin study.
Authors: Cohen-Cline H. , Beresford S.A.A. , Barrington W.E. , Matsueda R.L. , Wakefield J. , Duncan G.E. .
Source: Journal Of Epidemiology And Community Health, 2018 Mar; 72(3), p. 202-207.
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Estimating under-five mortality in space and time in a developing world context.
Authors: Wakefield J. , Fuglstad G.A. , Riebler A. , Godwin J. , Wilson K. , Clark S.J. .
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Hyak mortality monitoring system: innovative sampling and estimation methods - proof of concept by simulation.
Authors: Clark S.J. , Wakefield J. , McCormick T. , Ross M. .
Source: Global Health, Epidemiology And Genomics, 2018; 3, p. e3.
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Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data.
Authors: Fintzi J. , Cui X. , Wakefield J. , Minin V.N. .
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Temporal Trends In Geographic And Sociodemographic Disparities In Colorectal Cancer Among Medicare Patients, 1973-2010
Authors: Liang P.S. , Mayer J.D. , Wakefield J. , Ko C.W. .
Source: The Journal Of Rural Health : Official Journal Of The American Rural Health Association And The National Rural Health Care Association, 2016-08-31 00:00:00.0; , .
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Time Series Modeling Of Pathogen-specific Disease Probabilities With Subsampled Data
Authors: Fisher L. , Wakefield J. , Bauer C. , Self S. .
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Predictive Modeling Of Cholera Outbreaks In Bangladesh
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Bayesian Penalized Spline Models For The Analysis Of Spatio-temporal Count Data
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A Bayesian Method For Cluster Detection With Application To Brain And Breast Cancer In Puget Sound
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Using Small-area Estimation To Calculate The Prevalence Of Smoking By Subcounty Geographic Areas In King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013
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Comment: Getting Into Space With A Weight Problem
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Restricted Covariance Priors With Applications In Spatial Statistics
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Space-time Smoothing Of Complex Survey Data: Small Area Estimation For Child Mortality
Authors: Mercer L.D. , Wakefield J. , Pantazis A. , Lutambi A.M. , Masanja H. , Clark S. .
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Bayesian hierarchical models for smoothing in two-phase studies, with application to small area estimation.
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Bayesian inference for two-phase studies with categorical covariates.
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Incorporating Spatial Variability Within Epidemiological Studies Of Environmental Exposures
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Hand, Foot, And Mouth Disease In China: Patterns Of Spread And Transmissibility
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