生成型人工智能的应用和比较研究,用于对冠状病毒疾病的流行病预测
Zongjing Liang1, Gongcheng Liang2, Yun Kuang3
1Artificial Intelligence and Infectious Disease Prediction Models, School of Economics and Management, Guangxi Normal University, Guilin, CHN.
Cureus
|October 2, 2025
概括
生成型人工智能 (AI) 模型在预测像COVID-19这样的传染病方面显著优于传统方法. 这种人工智能驱动的方法为公共卫生监测和响应提供了更高的准确性和稳定性.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 人工智能的人工智能
背景情况:
- 大规模的冠状病毒爆发对全球健康和经济构成重大风险.
- 及时和准确的流行病预测对于有效的公共卫生反应至关重要.
- 现有的统计和机器学习模型在流行病预测的速度和精度方面存在局限性.
研究的目的:
- 将生成性人工智能 (AI) 模型的预测性能与传统的统计和机器学习模型进行比较.
- 评估人工智能驱动的短期传染病预测方法的准确性和稳定性.
- 探索生成性人工智能的潜力,以加强公共卫生监测系统.
主要方法:
- 对9个预测模型的比较分析:三个统计模型 (SMA,SES,霍尔特),三个机器学习模型 (KNN,RTree,MLP) 和三个生成AI模型 (ChatGPT,DeepSeek,Kimi).
- 利用来自美国,英国,德国和俄罗斯的每周COVID-19病例和死亡数据 (2020年3月 - 2023年4月).
- 使用平均绝对百分比误差 (MAPE),平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 评估预测准确度,用于一,两和三步预测.
主要成果:
- 与统计和机器学习模型相比,生成人工智能模型始终表现出更高的预测准确性.
- 基米模型在死亡预测方面实现了最低的错误率,在病例预测方面达到最低的错误率.
- 深度搜索和ChatGPT也表现出强的表现,在短期COVID-19预测中显著超过传统方法.
结论:
- 生成型人工智能模型为流行病预测提供了更高的预测准确性和稳定性.
- 这项研究强调了生成性AI在公共卫生决策和监测中的创新应用.
- 公共卫生当局应该考虑将生成AI整合到传染病监测,数据共享和资源分配战略中,以改善疫情应对.
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