The goal of this proposal is to develop new statistical methods for estimating prevalence and the size of at-risk groups in sexually transmitted infection epidemics. We also aim to estimate other policy-relevant quantities such as the number of orphans and children impacted, and treatment needs. We consider two types of epidemic: generalized epidemics, in which the disease is spread throughout the general population, and concentrated epidemics, in which the disease is largely confined to at-risk groups such as intravenous drug users, sex workers and men who have sex with men. Our goal is to develop methods appropriate for countries with sparse data, most of which are developing countries. For generalized epidemics, we propose a susceptible-infected model with a stochastic infection rate. We will develop a Bayesian approach to estimating the model from clinic data over time and sparse household surveys. We will extend the model to take account of changes in treatment availability, and to produce provincial as well as national estimates. For concentrated epidemics, we will first develop new integrated Bayesian methods for estimating the sizes of the main at-risk groups from fragmentary data, including mapping or hotspot data, behavioral surveillance data, program enrollment data and the overlaps between them. Much recent data comes from two relatively new network-based data collection methods, respondent-driven sampling (RDS) and the network scale-up method. We will develop methods for estimating unknown population size from multiple data sources, including RDS and network scale-up. We will then develop methods for estimating at-risk group size and prevalence over time, using a dynamic Bayesian model. We will produce publicly available software to implement our new methods and make them available to the research community and policy-makers.

Public Health Relevance

This project will develop new statistical methods for estimating prevalence and the size of at-risk groups in sexually transmitted infection epidemics as they change over time. It will also estimate policy-related quantities such as the number of orphans and children impacted, and treatment needs. The at-risk groups considered include intravenous drug users, sex workers and men who have sex with men. The methods will be applicable in countries with sparse data of variable quality, many of which are developing countries.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project (R01)
Project #
5R01HD054511-06
Application #
8404043
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Newcomer, Susan
Project Start
2007-01-01
Project End
2016-12-31
Budget Start
2013-01-01
Budget End
2013-12-31
Support Year
6
Fiscal Year
2013
Total Cost
$354,741
Indirect Cost
$117,491
Name
University of Washington
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Wheldon, Mark C; Raftery, Adrian E; Clark, Samuel J et al. (2016) Bayesian population reconstruction of female populations for less developed and more developed countries. Popul Stud (Camb) 70:21-37
Scrucca, Luca; Fop, Michael; Murphy, T Brendan et al. (2016) mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J 8:289-317
Onorante, Luca; Raftery, Adrian E (2016) Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window. Eur Econ Rev 81:2-14
Friel, Nial; Rastelli, Riccardo; Wyse, Jason et al. (2016) Interlocking directorates in Irish companies using a latent space model for bipartite networks. Proc Natl Acad Sci U S A 113:6629-34
Azose, Jonathan J; Ševčíková, Hana; Raftery, Adrian E (2016) Probabilistic population projections with migration uncertainty. Proc Natl Acad Sci U S A 113:6460-5
Bijak, Jakub; Alberts, Isabel; Alho, Juha et al. (2015) Letter to the Editor: Probabilistic population forecasts for informed decision making. J Off Stat 31:537-544
Scrucca, Luca; Raftery, Adrian E (2015) Improved initialisation of model-based clustering using Gaussian hierarchical partitions. Adv Data Anal Classif 9:447-460
Alkema, Leontine; Gerland, Patrick; Raftery, Adrian et al. (2015) The United Nations Probabilistic Population Projections: An Introduction to Demographic Forecasting with Uncertainty. Foresight (Colch) 2015:19-24
Wheldon, Mark C; Raftery, Adrian E; Clark, Samuel J et al. (2015) Bayesian Reconstruction of Two-Sex Populations by Age: Estimating Sex Ratios at Birth and Sex Ratios of Mortality. J R Stat Soc Ser A Stat Soc 178:977-1007
Maltiel, Rachael; Raftery, Adrian E; McCormick, Tyler H et al. (2015) Estimating Population Size Using the Network Scale Up Method. Ann Appl Stat 9:1247-1277

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