Title: EpiSimdemics: an Efficient Algorithm for Simulating the Spread of Infectious Disease over Large Realistic Social Networks
Speaker: Dr. Keith Bisset, NDSSL/VBI/VT
Date: Friday, November 7, 2008
Location: VTKnowledgeworksII, Room 1110
Preventing outbreaks of infectious disease such as Pandemic Influenza is a top public health priority. Traditional research in computational epidemiology has focused on rate-based differential-equation models on completely mixing populations.Although attractive for obtaining analytical results and basic insights into the epidemiological processes, the approach has certain well accepted weaknesses. Recently, researchers have begun the development of highly resolved individual-based models to understand the spread of infectious disease. The development of these models is
based on the advances in computing and information technology that make it possible to collect detailed data for developing these models and computing power to study the dynamic evolution of these models.Such models are the basis of modern computational epidemiology.
In this talk, we describe EpiSimdemics. EpiSmidemics is a highly scalable, parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is specifically designed to scale to social networks with 100-300 million individuals. The scaling is obtained by exploiting the semantics of within host disease evolution and between host disease propagation in large networks.