开发数据驱动的机器学习模型及其在预测登革热爆发中的潜在作用
Bushra Mazhar1, Nazish Mazhar Ali1, Farkhanda Manzoor2
1Department of Zoology, Government College University, Lahore, Pakistan.
Journal of vector borne diseases
|January 18, 2024
概括
预测登革热疫情对公共卫生至关重要. 整合气候和监测数据的机器学习模型提供了有效的预警系统,以防止疾病传播.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 登革热是全球主要的载体传播病毒感染,具有重大社会经济影响.
- 世界卫生组织 (WHO) 报告说,有25亿人面临风险,热带和亚热带地区的死亡率很高.
研究的目的:
- 用数据驱动的机器学习模型提供预测登革热爆发的概述.
- 突出大数据和人工智能用于登革热预测的整合.
主要方法:
- 使用现实数据,包括登革热监测,气候变量 (温度,降雨量,湿度,风速) 和流行病学数据.
- 采用机器学习算法来分析综合数据集以预测疫情.
- 开发一套有效的登革热发病预警系统.
主要成果:
- 使用气候和流行病学数据的创新方法提高了登革热发病率的监测和预测.
- 开发的系统可以作为一个有效的预警机制来预测疫情爆发.
- 早期警告使社区和当局能够及时实施预防措施.
结论:
- 基于数据的机器学习模型为预测登革热爆发提供了强大的工具.
- 将各种数据源与人工智能集成,可以增强预测能力.
- 有效的预警系统对于减轻登革热影响至关重要.
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