This workshop is a forum to discuss new insights into how data mining can play a bigger role in epidemiology and public health research. While the integration of data science methods into epidemiology has significant potential, it remains under studied. We aim to raise the profile of this emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results—in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the ‘trenches’.
Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work. Additionally, we are aiming to attract researchers and practitioners from the areas of mathematical epidemiology and public health, who are increasingly dealing with more complex models and novel data sources––these problems bring up novel challenges from a data mining and machine learning perspective.
To reflect the broad scope of work, we encourage submissions that span the spectrum from theoretical analysis to algorithms and implementation, to applications and empirical studies, from both data mining and public health viewpoints.
Topics of interest include, but are not limited to:
We invite the submission of regular research papers (6-8 pages) as well as work-in-progress, demo or position papers (2-4 pages). We recommend papers to be formatted according to the standard double-column ACM Proceedings Style. All papers will be peer reviewed and single-blinded. Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session, and some set may also be chosen for oral presentation. The accepted papers will be published online and will not be considered archival.
For paper submission, please proceed to the submission website.