This one-day workshop is held in conjunction with ACM SIGKDD 2020. Many of the organizers are MIDAS members!
Call for papers is currently open. Submission deadline is June 15, 2020. See event website for details.
Background: There is an urgent need to develop sound theoretical principles and transformative computational approaches that will allow us to address the escalating threat of current and future pandemics. Data mining and knowledge discovery have an important role to play in this regard. Different aspects of infectious disease modeling, analysis and control have traditionally been studied within the confines of individual disciplines, such as mathematical epidemiology and public health, and data mining and machine learning. Coupled with increasing data generation across multiple domains (like electronic medical records and social media), there is a clear need for analyzing them to inform public health policies and outcomes.
– data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work.
– researchers and practitioners from the areas of mathematical epidemiology and public health.
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
- Data-driven advances in control and optimization (like immunization)
- Forecasting disease outcomes with clinical data
- 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