从横截面调查数据重建多菌株病原体相互作用,通过统计网络推理
Irene Man1,2, Elisa Benincà1, Mirjam E Kretzschmar2
1Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Journal of the Royal Society, Interface
|August 9, 2023
概括
了解病原体菌株相互作用对于传染病控制至关重要. 这项研究表明,统计网络推断可以从调查数据中准确地绘制这些复杂,异构的关系.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 传染病的动态传染病的动态.
背景情况:
- 传染病经常涉及多种病原体物种或菌株.
- 推断病原体相互作用的现有方法是有限的,经常忽视间接效应,导致偏见的结果.
- 准确了解病原体相互作用对于有效的疾病干预策略至关重要.
研究的目的:
- 评估统计网络推断来重建多种病原体菌株之间的异质相互作用.
- 通过使用横截面调查数据,评估这些方法在宿主中检测病原体菌株的联合存在/缺席模式的能力.
主要方法:
- 将各种网络模型应用于模拟的调查数据,这些数据代表了具有潜在相互作用的特有感染状态.
- 研究了对样本大小的规范化和惩罚技术对相互作用网络重建的影响.
- 评估了宿主异质性的影响,并使用个人级别的风险因素探索了纠正.
主要成果:
- 统计网络推断估计器汇聚到真实交互,在模拟中表现出令人满意的性能.
- 实现了复杂交互网络的准确重建,特别是对样本大小进行规范化或惩罚.
- 主体异质性影响了表现,但通过纠正个人级别的风险因素,成功克服了这一问题.
结论:
- 统计网络推断是一种强大的工具,可以从人口层面调查数据中检测多菌株病原体相互作用.
- 开发的方法可以准确地重建异质相互作用网络,考虑间接影响.
- 这种方法具有显著的潜力,可以改善流行病学研究,并为目标疾病干预提供信息.
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