用深度学习与大模型预测印度传染病爆发严重程度。
Infectious Disease Forecasting in India using LLM's and Deep Learning
- 结合历史疾病数据与十年气候数据，构建预测模型。
- 通过多源数据融合提升对疫情严重性的预判能力。
- 适合公共卫生决策者与流行病研究者参考。
过去多次无法控制的疾病暴发暴露了全球医疗体系的诸多弱点。尽管技术进步加速了疫苗研发，但亟需加强对大规模疫情的预防与预测。早期发现和干预可显著降低疫情对公共健康的影响，并增强医疗系统的韧性。疾病传播机制复杂、受多种直接或间接因素影响，且传统方法存在局限，成为采取预防措施的主要障碍。本文利用深度学习算法与大语言模型，基于印度过去数种疾病传播的历史数据及近十年气候数据，探索建立未来疫情爆发的稳健预测系统，旨在为防控提供科学支持。
原文摘要 · Abstract (English)
Many uncontrollable disease outbreaks of the past exposed several vulnerabilities in the healthcare systems worldwide. While advancements in technology assisted in the rapid creation of the vaccinations, there needs to be a pressing focus on the prevention and prediction of such massive outbreaks. Early detection and intervention of an outbreak can drastically reduce its impact on public health while also making the healthcare system more resilient. The complexity of disease transmission dynamics, influence of various directly and indirectly related factors and limitations of traditional approaches are the main bottlenecks in taking preventive actions. Specifically, this paper implements deep learning algorithms and LLM's to predict the severity of infectious disease outbreaks. Utilizing the historic data of several diseases that have spread in India and the climatic data spanning the past decade, the insights from our research aim to assist in creating a robust predictive system for any outbreaks in the future.
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