一个网络分析和支持向量回归方法用于可视化和预测马来西亚COVID-19疫情的爆发
Siti Nurhidayah Sharin1, Mohamad Khairil Radzali2, Muhamad Shirwan Abdullah Sani3,4,5
1Halal Products Research Institute, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
Healthcare analytics (New York, N.Y.)
|July 31, 2023
概括
网络分析和支持向量回归可视化了马来西亚的COVID-19传播和预测病例. 这些人工智能方法帮助马来西亚当局实施预防措施,防止冠状病毒疾病19的流行.
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:
- 冠状病毒疾病19 (COVID-19) 流行病对公共卫生监测和应对提出了重大挑战.
- 有效的可视化和疾病传播的预测对于实施及时干预至关重要.
研究的目的:
- 使用网络分析 (NA) 对马来西亚COVID-19大流行传播的相关性和可视化.
- 使用支持向量回归 (SVR) 预测累积的COVID-19确诊和死亡病例.
- 评估NA和SVR作为人工智能工具在流行病风险管理中的实用性.
主要方法:
- 斯皮尔曼等级系数用于网络分析 (NA),以可视化疾病传播模式.
- 支持向量回归 (SVR) 模型被用来预测未来的COVID-19病例和死亡人数.
- 2020年7月至2021年6月间马来西亚的数据构成了分析的基础.
主要成果:
- 网络分析显示,马来西亚各州之间的连接性日益增加,在2021年第二季度观察到最复杂的COVID-19传播网络.
- 该SVR模型成功预测了2021年下半年马来西亚未来的COVID-19病例和死亡人数.
- 该研究证明了NA和SVR在可视化传播和预测流行病风险方面的有效性.
结论:
- 网络分析和支持向量回归提供了有价值的,相对简单的人工智能技术,用于理解和预测流行病动态.
- 这些发现支持卫生当局使用这些方法来制定有针对性的预防措施,特别是在人口密地区.
- 本研究提供了对利用数据驱动方法来加强马来西亚的COVID-19应对策略的见解.
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