Real world assessment of HIV risk is a slow process that relies on static data from national census statistics and other surveys. In some cases this static data is complemented with local data that HIV clinics might have on their patient population, but this only represents the specific subset of HIV at-risk individuals that are already seen at clinics for treatment or testing.

However lack of early testing in many HIV at risk individuals, hinders the ability to account for many people who are at risk. Our goal is to reduce this time gap and build a near real-time intervention system to help doctors and researchers to respond to HIV risk effectively.

Our work is focused on integrating into a unique pipeline natural language processing, machine learning and network features to filter data publicly available, and confidentially collected from Twitter. The ultimate goal of the developed infrastructure is to help clinicians in characterizing the structure of social networks of local population who may be at risk of acquiring or transmitting HIV infection in the San Diego Area, and to be able to that in real-time. Our integrated platforms allows clinicians to visualize this structure and identify patterns in online social media communication and provide an additional tool to inform targeted interventions to reduce HIV risk.

More Info here: https://pircnet.ucsd.edu/

Funding and External Collaborations

PIRC-Net is a project in collaboration between the CSE Department, the Center for AIDS Research, and the Antiviral Research Center within the School of Medicine at UC San DIego, and has been funded by the San Diego CFAR, and the UCSD Office of Research Affair.


Nadir Weibel
Nadir Weibel
Professor of Computer Science and Engineering