Call for Papers

The 4th epiDAMIK@SIGKDD 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’. The current COVID-19 pandemic has only showcased the urgency and importance of this area.

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 science and machine learning perspective.

The past iterations of the workshop were co-located with SIGKDD 2018, SIGKDD 2019 (also part of ‘health days’), and SIGKDD 2020. These were a great success with insightful contributed works as well as high-quality keynotes.

To reflect the broad scope of work, we encourage submissions that span the spectrum from theoretical analysis, 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:

• Epidemiologically-relevant data collection and curation
• Advances in modeling, simulation and calibration of disease spread models
• Syndromic surveillance using social media, search and other data sources
• Challenges in model validation against ground truth
• Outbreak detection and inference
• Visualization of epidemiological data
• Planning for public health policy
• Role of open source data and community in epidemiology
• Data-driven advances in control and optimization (like immunization)
• Forecasting disease outcomes including COVID-19 projections
• Graph mining and network science approaches to epidemiology
• Crowdsourced methods for detection and forecasting
• Use of novel datasets for prediction and analysis (including EHR records)
• Genomic analyses related to outbreak science (e.g., phylogenetics)
• Data mining for hospital acquired infections like C.Diff, MRSA etc.
• Identifying health behaviors
• Handling missing and noisy data
• Disease forecasting challenge (like the CDC Flu Challenge) experiences
• Interpretable and expert-driven AI for public health
• Any late-breaking work on the COVID-19 epidemic
• Experiences of real-time forecasting
• Infodemic, misinformation, and disinformation

We invite the submission of full regular research papers (6-8 pages) as well as short, work-in-progress, demo or position papers (2-4 pages). Short summary versions of recently published major papers (2-4 pages) are also welcome.

We recommend papers to be formatted according to the standard double-column ACM Proceedings Style. All papers should contain the name of authors and their affiliations. Authors whose papers are accepted to the workshop will have the opportunity to participate in a virtual poster session, and some may also be chosen for oral presentation. There are no restrictions on already submitted work or authors simultaneously posting their manuscripts to any pre-print server. The conference and the workshop will be completely virtual, with virtual presentations for accepted papers and keynotes. The accepted papers will be made available online but will not be considered archival. The authors are free to resubmit the paper to pre-print servers and future conferences and journals.

For paper submission, please proceed to the submission website.
Please send any enquiries to epidamik@gmail.com.

Important Dates