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Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint)
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An editorial introducing the AAAI/JAIR special track on AI and COVID-19, summarizing the four accepted papers on point processes, symptom extraction, agent-based RL mitigation policies, and epidemic control toolboxes.
Problem
Lack of a consolidated view of how AI and data science can address COVID-19 challenges such as outbreak prediction, risk modeling, testing, drug and vaccine development.
Approach
Editorial that introduces a special track collecting four accepted papers on AI applied to COVID-19 and summarizes their contributions; no new method is proposed.
Key results
- The special track received 24 submissions and accepted four papers.
- Accepted papers cover Hidden Hawkes Geometry point process modeling, NLP symptom extraction from ED notes, agent-based Markov modeling with RL for mitigation policies, and the EpidemiOptim toolbox.
Limitations
- Editorial only summarizes papers; no independent evaluation.
- Covers just four accepted papers from 24 submissions.
Closest papers by meaning
- AI for Equitable, Data-Driven Decisions in Public Health (AAAI 2023 · similarity 0.85)
- Unlocking the Potential of Global Human Expertise (NeurIPS 2024 · similarity 0.78)
- Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria (AAAI 2024 · similarity 0.76)
- Dynamics-Based Feature Augmentation of Graph Neural Networks for Variant Emergence Prediction (AAAI 2025 · similarity 0.76)
- Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread (AAAI 2024 · similarity 0.75)
- Multilingual Medical Language Models: A Path to Improving Lay Health Worker Effectiveness (Student Abstract) (AAAI 2024 · similarity 0.75)
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