从跨设置和上下文依赖的相互侵入性数据中推断对交互的对交互.
Thi Minh Thao Le1, Sten Madec2, Erida Gjini3
1Department of Mathematics and Statistics, Masaryk University, Brno, Czech Republic.
Bulletin of mathematical biology
|May 21, 2025
概括
我们开发了一种使用人口数据绘制细菌菌株相互作用的新方法. 这种方法揭示了Streptococcus pneumoniae血清型的复杂相互作用网络,有助于理解疾病动态和干预措施.
科学领域:
- 微生物学和流行病学
- 数学建模的数学建模
- 人口遗传学 人口遗传学
背景情况:
- 了解微生物种群动态需要描述不同菌株之间的相互作用.
- 以前的方法往往难以从横截面数据中推断出复杂的交互网络.
研究的目的:
- 开发和验证一种新的计算框架,用于估计从人口水平频率的双向菌株相互作用.
- 将这种方法应用于来自全球不同环境的Streptococcus pneumoniae血清型数据.
主要方法:
- 利用了从多菌株SIS模型中衍生出的复制者动态,并进行了共殖民.
- 来自五个国家的Streptococcus pneumoniae血清型频率的综合流行病学数据.
- 采用基本复制数 (R0),平均全球敏感性 (k) 和对偏差 (αij) 来模型相互作用.
主要成果:
- 成功推断出超过70%的92x92 Streptococcus pneumoniae血清型相互作用矩阵.
- 证明了血清型内部和血清型之间的相互作用系数表现出单模分布.
- 展示了该方法的概念证明,用于从横截面数据推断多种相互作用.
结论:
- 拟议的框架提供了肺炎球菌血清型相互作用的高分辨率地图.
- 这种方法可以对复杂微生物生态系统的干预效应进行可靠的研究.
- 该方法适用于横截面和纵向数据分析.
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