DSSEEA 2013 - Workshop on Discrete, stochastic simulations with an emphasis on epidemiological application

For a myriad of investigations into natural processes, computational resources have been a bottleneck to the validation of scientific hypotheses about the fundamental nature of those processes. In fields such as epidemiology, this bottleneck has profound consequences for the development of strategies to combat the spread of diseases. The rapid advancement and availability of computational resources have provided the ability to test hypotheses about natural processes through the construction of high-dimensional discrete, stochastic models.
The objective of this workshop is to focus on that frontier of computational science: the intersection of numerical algorithms, high-dimensional complex systems, and an ever-increasing computational resource pool. We aim to bring together scientists from the broad field of computational science in academia, industry, and society.
This workshop will also have special emphasis on, but not be limited to, discrete models and numerical methods that focus on epidemiological processes. Bringing together specialists in computation, modeling, and analysis, the workshop will help foster the development of new methods and models through collaboration in this cross-disciplinary research field.
Specific topics include (but are not limited to):
- Discrete models for disease propagation and vaccination campaigns
- High-dimensional epidemiological models
- Epidemiological applications for near-eradication regimes
- Modeling of malaria, polio, HIV, tuberculosis, and neglected tropical diseases
- Models for pathogen life cycle, within-host dynamics, and vector populations and transmission
- Spatially inhomogeneous processes, e.g. reaction-diffusion systems
- Rare-event probability estimation, importance sampling, variance reduction
- Epidemiological and biochemical modeling using Monte Carlo methods or stochastic differential equations
- Model formulation and parameter fitting methods from empirical data
- Sensitivity analysis for stochastic numerical methods
- Dimensional reduction and numerical methods for the master equation
- Compressive sensing and sparse data analysis associated with field observations.