在基于机器学习的预测登革热的方法中,气象因素不能被忽视,这是一个系统的审查
Lanlan Fang1, Wan Hu1, Guixia Pan2,3
1Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
International journal of biometeorology
|December 27, 2023
概括
机器学习准确地预测登革热发病率,气象因素如温度和降雨量是关键预测因素. 支持矢量机 (SVM) 和长短期内存 (LSTM) 是早期预警系统的高性能模型.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 登革热发病率预测越来越多地利用机器学习.
- 在研究中的预测因素和模型中存在显著的变化.
- 需要进行系统审查,以巩固当前的知识.
结论:
- 气象因素对于准确的登革热预测至关重要.
- 标识到的最有效的机器学习模型是SVM和LSTM算法.
- 这一审查支持加强登革热早期预警系统和传染病预测.
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