EpiC: A Computational Infrastructure for Epidemics Research

Epidemic and diffusion processes are of great importance in the modeling, forecast, and analysis of a wide range of problems relevant to public health. The spread of infectious diseases remains a serious medical burden around the world and causes an estimated 15 million deaths each year. The epidemic paradigm can also be fruitfully applied to a wide range of "social contagion processes" where the acquisition or increase of a behavior or cultural habit is related to social contacts between the "uninfected population" and the "infected population". The epidemic approach has already proven useful in understanding social behaviors such as tobacco, alcohol, and heroin use. The study of epidemic processes is a particularly challenging undertaking, because it requires a truly interdisciplinary effort that combines and integrates knowledge, datasets, and techniques developed in epidemiology, medicine, physics, computer science, and social and behavioral sciences, among others.

This project will the design, implementation, deployment, and maintenance of a computational infrastructure for epidemics research, called the Epidemics Cyberinfrastructure (EpiC). EpiC will support the integration of large numbers of complex datasets at a variety of levels, e.g., population density, patient records, and social behavior, as well as state of the art analysis, modeling, and visualization tools capable of dealing with non-linear and complex phenomena emerging in large populations. Unlike other cyberinfrastructure (CI) efforts, EpiC proposes the design, implementation, validation, and community support of a "bazaar" - like infrastructure that will be a marketplace for epidemics resources. EpiC will provide members of the scientific research community at large with the means to carry out data analysis, modeling, and visualization at multiple levels of analysis of social contagion and epidemic processes.

Specifically, this project will
  1. Develop a novel type of cyberinfrastructure, called EpiC CIShell, resembling an "empty shell" that can be filled with epidemics-related resources such as datasets, algorithms, and visualization components. This novel type of CI supports the easy assembly of EpiC Tools aimed at specific research projects for a few weeks, months, or years.
  2. Setup and maintain an EpiC Marketplace that serves as an index to EpiC resources. The marketplace will draw on the successful interaction designs that drive popular file and content sharing community sites like Flickr, YouTube, or Wikipedia. However, instead of sharing images, movies, or encyclopedia entries, scholars will use EpiC to share datasets, algorithms, visualizations, and other items relevant to the study of epidemics.
  3. Apply EpiC prototypically to tackle research questions about computational approaches to epidemic modeling. This lead to the development of EpiC tools that are specifically tailored to the forecast of emerging disease spreading on the global scale and social contagion processes in the area of smoking habits. The latest EpiC tool releases and tutorials are available at http://epic.wiki.cns.iu.edu

The overarching goals of EpiC are the improvement and facilitation of multi-scale analysis of social data integrated into dynamic systems modeling, agent-based modeling, and other simulation techniques for epidemic processes; the direct transfer of knowledge and results from fields of specialist research (computational modeling, mathematical epidemiology, large scale visualization etc.) to the wider scientific community; the development of a cyberinfrastructure technology that is open, usable, extensible, and sustainable.


Supported in part by the NIH RM-07-004 award.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Institutes of Health (NIH).