The epiDAMIK workshop serves as a platform for advancing the utilization of data-driven methods in the fields of epidemiology and public health research. These fields have seen relatively limited exploration of data-driven approaches compared to other disciplines. Therefore, our primary objective is to foster the growth and recognition of the emerging discipline of data-driven and computational epidemiology, providing a valuable avenue for sharing state-of-the-art research and ongoing projects. The workshop also seeks to showcase results that are not typically presented at major computing conferences, including valuable insights gained from practical experiences. Our target audience encompasses researchers in AI, machine learning, and data science from both academia and industry, who have a keen interest in applying their work to epidemiological and public health contexts. Additionally, we welcome practitioners from mathematical epidemiology and public health, as their expertise and contributions greatly enrich the discussions. Homepage: https://epidamik.github.io/.
Alexander Rodríguez, B. Adhikari, A. Srivastava et al.· Proceedings of the 32nd ACM...· 0 citations
HyperODE is introduced, a surrogate capable of operating across an entire class of approximately mass-conserving compartmental models without retraining, by mapping the structure of ordinary differential equations into directed hypergraphs, which decouples the functional form of system interactions from the neural network architecture.
A. Srivastava· 0 citations
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