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Public Health Dynamics Seminars

March 13, 2017 - Real-Time Social Internet Data to Guide Disease Forecasting Models

Sara Del Valle, PhD

Deputy Group Leader, Analytics Intelligence and Technology Division,
Los Alamos National Laboratory, Los Alamos, NM

Monday, March 13, 2017
12:00 – 1:00 PM
A521 Crabtree Hall – Pitt Public Health


Globalization has created complex problems that can no longer be adequately forecasted and mitigated using traditional data analysis techniques and data sources. Disease spread is a major health concern around the world and is compounded by the increasing globalization of our society. As such, epidemiological modeling approaches need to account for rapid changes in human behavior and community perceptions. Social media has recently played a crucial role in informing and changing the response of people to the spread of infectious diseases. Recent events, such as the 2014-2015 Ebola epidemic and the 2015-2016 Zika virus epidemic, have highlighted the importance of reliable disease forecasting for decision support. The speaker will discuss a framework that combines clinical surveillance data with social Internet data and mathematical models to provide probabilistic forecasts of disease incidence and will demonstrate the value of Internet data and the real-time utility of our approach.

About the speaker:

Sara Del Valle, PhDSara Del Valle is a scientist at Los Alamos National Laboratory. She earned a PhD in Applied Mathematics and Computational Sciences from the University of Iowa in 2005. Her research focuses on using mathematical and computational approaches to improve our understanding of human behavior and its impact on disease spread. She has developed models for many diseases including smallpox, anthrax, HIV, pertussis, MERS-CoV, malaria, influenza, Ebola, and Zika. Her research interests also include developing and analyzing large-scale agent-based discrete event simulations for disease dynamics and social networks. Most recently, she has been investigating the role of Internet data on monitoring and forecasting disease spread and disease-related behaviors.







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