使用机器学习和云计算预测COVID-19流行病的增长和趋势
Shreshth Tuli1, Shikhar Tuli2, Rakesh Tuli3
1Department of Computer Science and Engineering, Indian Institute of Technology Delhi, India.
机器学习和云计算为跟踪COVID-19 (冠状病毒疾病2019) 和预测其传播提供了有效的工具. 使用代权重的改进数学模型为积极的公共卫生战略提供了准确的实时流行病增长预测.
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:
- 由于COVID-19 (由SARS-CoV-2引起) 的流行病,造成了严重的全球健康危机.
- 迅速增加的发病率需要先进的追踪和预测方法.
- 机器学习 (ML) 和云计算为流行病管理提供了潜在的解决方案.
研究的目的:
- 应用一个改进的数学模型来分析和预测COVID-19流行病的增长.
- 开发基于ML的预测框架,用于评估全球COVID-19威胁.
- 利用云计算进行准确的,实时的流行病行为预测.
主要方法:
- 利用了一种改进的数学模型,将代权重纳入通用反向韦布尔分布适配.
- 在云计算平台上开发和部署基于ML的预测框架.
- 采用数据驱动的方法来提高预测准确度.
主要成果:
- 证明代加权提高了通用反向韦布尔分布在流行病建模中的适应性.
- 通过云部署的ML模型实现了更准确和实时的流行病增长行为的预测.
- 验证了数据驱动方法对主动公共卫生响应的有用性.
结论:
- 开发的基于ML的预测框架为了解和管理COVID-19大流行提供了有价值的工具.
- 准确,实时的流行病预测对于有效的政府和公民反应至关重要.
- 确定了先进的流行病建模的进一步研究机会和实际应用.
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