CFP: epiDAMIK @ KDD 2022 Workshop on Epi meets Data
Alexander Rodriguez
Dear colleague,
The 5th 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’.
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.
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 will be peer reviewed and single-blinded, thus, they 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 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 accepted papers will be made available online but will not be considered archival. Therefore, authors are free to resubmit the paper to pre-print servers and future conferences and journals.
Please send any enquiries to epid...@gmail.com and cc bijaya-...@uiowa.edu and arodr...@gatech.edu
Important Dates
All deadlines are set at 11:59 PM Easter Daylight Time.
Submission site open: April 20, 2022
Workshop paper submissions: May 26, 2022
Workshop paper notifications: June 20, 2022
Camera-ready papers due: July 10, 2022
Workshop date: August 15, 2022
You can find more information at https://epidamik.github.io/ and follow us on Twitter @EpidamikW
We look forward to your participation.
Sincerely,
The organizing team:
Bijaya Adhikari (University of Iowa), Amulya Yadav (Penn State), Sen Pei (Columbia), Ajitesh Srivastava (USC), Sarah Kefayati (IBM), Alexander Rodriguez (Georgia Tech), Marie-Laure Charpignon (MIT).