To achieve critical health milestones (e.g., National HIV/AIDS Strategy1), the public health system needs methods to predict HIV epidemiology within a region. An unexpected surge of new diagnoses in Miami, FL or Austin, IN, may well be avoided if public health officials are able to forecast these changes and to intervene in anticipation. However, modeling approaches are underutilized as mainstream tools to aid public health decisions,2 owing to barriers including (a) unavailability of user-friendly methods that consider the spatiotemporal relations among predictors of HIV transmission dynamics, (b) lack of inclusion of powerful big social media data to gauge population norms and diffusion of information about HIV testing and prevention services, (c) lack of integration of disperse yet relevant sources of data to predict HIV epidemiology, (d) lack of visualization tools for the results of that integration, and (e) lack of models to gauge impact of new interventions (e.g., an HIV vaccine), or changes in current interventions. In this application, we propose methods that, if successful, will allow public health officials and the scientific community to make such refined predictions and thereby to plan for interventions such as PrEP (PreExposure Prophylaxis). The project will rely on existing but disperse sources of regional epidemiological, socio-structural, social media, and intervention data to produce models and Cyber-GIS-HIV, a tool that can be used by public health officials and researchers. The tool will analyze data and produce results in an integrated output identifying vulnerable regions, and predicting future pockets of vulnerability and the effects of changes in intervention policy. We will integrate epidemiological and biomedical service data recorded by health departments, data from the US Census, the American Community Survey, the American Men Internet Survey, transmission network datasets, social media data, and effect sizes from new interventions to derive predictions. We will also develop new methods for social media analyses and compare spatio-temporal modeling techniques. The system will offer recommendations about service allocation for a zip code, a county, and a region, set to introduce services equally across areas, or to target the areas that would give the most improvement for the state as a whole. The University of Illinois, Emory University, and the University at Albany offer the ideal social science, public health, and computing infrastructure for this project. The team (Illinois: Albarracin, Chan, Li, Sundaram, and Wang; Albany: Holtgrave) has developed cutting-edge big-data models to predict HIV and flu, as well as original spatiotemporal analysis and existing state-of-the-art CyberGIS tools. Dr. Do at Emory served in the division of HIV surveillance epidemiology at CDC for two decades and is now a faculty member. In addition, health department personnel will be involved in designing and in testing CyberGIS-HIV during the last year of the project, if the methods pass a preestablished set of Go/No Go criteria.
Our project is designed to optimize HIV prevention research by advancing modeling and simulation models to encapsulate factors from socio-structural determinants of health, to transmission dynamics, to biomedical service utilization, to social norms, into a single approach. This project will develop models to predict HIV epidemiology and make recommendations about service allocation for a zip code, a county, and a region.
