Summary of Infectious Disease Forecasting in India Using Llm’s and Deep Learning, by Chaitya Shah et al.
Infectious Disease Forecasting in India using LLM’s and Deep Learning
by Chaitya Shah, Kashish Gandhi, Javal Shah, Kreena Shah, Nilesh Patil, Kiran Bhowmick
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing issue of predicting and preventing massive disease outbreaks. By leveraging deep learning algorithms and large language models (LLMs), researchers aim to create a robust predictive system that can mitigate the impact of such outbreaks on public health. The study focuses on infectious diseases in India, using historic data and climatic patterns spanning a decade to develop insights for future outbreak prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses deep learning algorithms and LLMs to predict the severity of infectious disease outbreaks. It collects historic data from several diseases that have spread in India and combines it with climatic data from the past decade to develop insights for creating a robust predictive system. |
Keywords
* Artificial intelligence * Deep learning
