制定一个框架,从提交的SARS-CoV-2基因组样本中建立"黄金标准"疫情数据
Yannan Shen1, Russell Steele2, Philip Abdelmalik3
1Department of Epidemiology, Biostatistics and Occupation Health, McGill University, Montreal, Quebec, Canada.
概括
基因组监测数据可以确定新的呼吸道病毒变种的疫情发病日期. 使用基因组提交数据的贝叶斯在线变化点检测算法 (BOCP) 框架提供了可靠的基因组提交数据.
科学领域:
- *基因组流行病学和传染病监测.
- * 计算生物学和公共卫生的统计建模.
背景情况:
- * 针对呼吸道病毒的基因组数据提交追踪了变种的出现和传播.
- * 基因组数据提交的延迟阻碍了未来的监测,但可以帮助评估其他监测系统.
- * 关于利用基因组提交数据用于监视中异常检测的研究有限.
研究的目的:
- * 制定一个框架,使用全球基因组提交数据确定"黄金标准"疫情发病日期.
- * 为此目的评估贝叶斯在线变化点检测算法 (BOCP) 的实用性.
- * 评估数据转换和参数设置对变化点检测的影响.
主要方法:
- * 应用贝叶斯在线变化点检测算法 (BOCP) 来检测提交的基因组样本的增加.
- *利用来自多个国家的基因组提交数据来确定疫情发病日期.
- * 对比了BOCP的不同数据转换技术和算法参数值.
主要成果:
- *BOCP成功地确定了大量的变化点,这些变化点表明疫情爆发.
- *变化点检测结果强大,对参数值变化没有高度敏感.
- *发现数据转换对于有效的变化点检测至关重要.
结论:
- *使用全球基因组提交数据和BOCP的框架可以确定可靠的"黄金标准"疫情发病日期.
- *这种方法增强了对基因组监测数据的评估,以追踪新变异.
- *这些发现支持使用基因组数据进行追溯分析和监控系统的验证.
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