The purpose of this Mentored Research Scientist Development Award (K01) is threefold. This will allow me to: 1) broaden my understanding of the social epidemiology of HIV among drug users and sex workers in a new cultural and environmental context, 2) learn a new methodology in computational modeling of infectious diseases, and 3) facilitate my transition to becoming a productive NIH-funded independent Investigator at the University of California San Diego. The training aims will be accomplished through a combination of specific workshops and coursework, a hands-on research project, and one-on-one mentoring with a Training Committee comprised of experts in areas of HIV-transmission networks, computational modeling, GIS and geospatial analysis, and HIV social epidemiology research in the US/Mexico border regions. This research will be accomplished by conducting multidisciplinary studies on the computational modeling of HIV/STI transmission to construct, validate, and calibrate a computer simulation model reflecting the complex interdependences between individual behavior and environmental influence on HIV/STI. This project will leverage data from an existing NIH/NIDA-funded R01, DA028692-01 (""""""""Evolving HIV/STI risk environments of female sex workers (FSWs) on the Mexico/US border"""""""" aka Proyecto Mapa de Salud, PI Kimberly Brouwer) in order to capitalize on the infrastructure and expertise represented by that project. The US/Mexico border is experiencing a burgeoning HIV epidemic, concentrated among high-risk groups such as drug users and FSWs. Computational modeling has been routinely used in areas such as disease epidemiology, health care capacity, and patient flows in emergency care to capture the complex behavior of system but have been slower to be adopted in health-related behavioral and social science research. Computational modeling offers the unique opportunity to address the casual mechanism of HIV/STI risk by examining cyclic relationship of individuals interacting within their environment and in turn their environment shaping risk-related behaviors. The proposed research will allow me to apply newly acquired skills in computational modeling to develop a robust simulation of HIV/STI transmission as we change the context of risks including factors measured at the behavioral, social, physical, and geographical level. This model will be validated and calibrated with extant data sampled from a particularly high-risk population in Tijuana where HIV prevalence among women participating in sex work is 21%. Findings from this research will enhance scientific understanding on which environmental determinants affect population-levels of HIV/STI transmission and could have major implications for the types of evidence that are used to make policy decision and public health interventions. Further, developing skills in computational modeling will uniquely position me as only one of a handful of quantitative methodological researchers in the field of drug abuse possessing such skills, and the only one in the Division of Global Public Health at the University of California, San Diego.
The US/Mexico border is home to an evolving HIV epidemic among vulnerable groups such as drug users and female sex workers but little social epidemiological research has addressed the role of complex interdependencies between individual behavior and environmental influence by modeling the bidirectional relationships between these factors. Findings from the proposed research will enhance scientific understanding of environmental determinants affecting population-levels of HIV/STI transmission and could have major implications for the types of evidence that are used to make policy decisions and public health interventions. Further developing skills in computational modeling of HIV/STI transmission will uniquely position the candidate as one of only a handful of quantitative methodological researchers in the field of drug abuse possessing such skills, and the only one in the Division of Global Public Health at the University of California, San Diego.
Showing the most recent 10 out of 22 publications
