基于空间可视化和机器学习的传染病分析和预测
Yunyun Cheng1, Yanping Bai2, Jing Yang3
1School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China.
Scientific reports
|November 20, 2024
概括
对传染病的早期预警系统至关重要. 一个新的多算法融合模型有效地预测了COVID-19并显示了艾滋病和结核病等其他疾病的前景.
科学领域:
- 流行病学和公共卫生.
- 计算生物学和生物信息学
- 数据科学和机器学习
背景情况:
- 传染病是全球健康面临的重大挑战,每年都会出现新的病毒.
- 早期发现和预测传染病爆发对于减轻社会经济影响至关重要.
- 随着COVID-19的爆发,人们越来越需要强大的预测模型工具.
研究的目的:
- 为了分析COVID-19在中国的空间模式.
- 评估各种机器学习模型对传染病数据的预测性能.
- 开发和验证用于传染病预测的优质集体学习模型.
主要方法:
- 使用ArcGIS进行空间分析,包括全球和本地空间自相关,以及重心轨迹迁移.
- 单个预测模型的比较分析:自回归集成移动平均 (ARIMA),极端学习机器 (ELM),支持向量回归 (SVR),波形神经网络 (Wavelet),循环神经网络 (RNN) 和长短期记忆 (LSTM).
- 使用辐射基函数网络 (RBF) 作为元学习器开发堆叠组合模型,并使用粒子群集优化 (PSO) 进行优化.
主要成果:
- 在中国确定高发病率的COVID-19地区.
- 证明集体堆叠模型在预测广东省COVID-19病例方面表现优于单个模型.
- 通过成功预测艾滋病和肺结核病 (PTB) 病例,验证整体模型的广泛适用性.
结论:
- 开发的多算法融合模型为传染病提供了增强的概括性和预测准确性.
- 空间分析有助于识别针对性公共卫生干预的高风险地区.
- 这种方法为新兴和现有的传染病的早期预警和管理提供了有价值的框架.
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