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.
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.
| Hernández, Belinda; Raftery, Adrian E; Pennington, Stephen R et al. (2018) Bayesian Additive Regression Trees using Bayesian Model Averaging. Stat Comput 28:869-890 |
| Sharrow, David J; Godwin, Jessica; He, Yanjun et al. (2018) Probabilistic population projections for countries with generalized HIV/AIDS epidemics. Popul Stud (Camb) 72:1-15 |
| Scrucca, Luca; Raftery, Adrian E (2018) clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R. J Stat Softw 84: |
| Godwin, Jessica; Raftery, Adrian E (2017) Bayesian projection of life expectancy accounting for the HIV/AIDS epidemic. Demogr Res 37:1549-1610 |
| Raftery, Adrian E; Zimmer, Alec; Frierson, Dargan M W et al. (2017) Less Than 2 °C Warming by 2100 Unlikely. Nat Clim Chang 7:637-641 |
| Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao et al. (2017) fastBMA: scalable network inference and transitive reduction. Gigascience 6:1-10 |
| McCormick, Tyler H; Lee, Hedwig; Cesare, Nina et al. (2017) Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing. Sociol Methods Res 46:390-421 |
| Onorante, Luca; Raftery, Adrian E (2016) Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window. Eur Econ Rev 81:2-14 |
| Šev?íková, Hana; Raftery, Adrian E (2016) bayesPop: Probabilistic Population Projections. J Stat Softw 75: |
| 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 |
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