环境病原体监测的机制建模和估计框架
Matthew Wascher1, Colin J Klaus2, Chance Alvarado3
1Division of Epidemiology, College of Public Health, The Ohio State University, United States of America; Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, United States of America.
Mathematical biosciences
|August 22, 2024
概括
环境病原体监测,就像SARS-CoV-2一样,面临着由于可变流出的挑战. 这项研究开发了一个模型,将环境数据与感染个体联系起来,改善公共卫生洞察力.
科学领域:
- 环境微生物学环境微生物学
- 流行病学 流行病学
- 数学建模的数学建模
背景情况:
- 环境病原体监测对于疾病监测至关重要,特别是针对SARS-CoV-2.
- 感染个体之间病原体分泌的变化使数据解释复杂化.
- 将环境数据整合到公共卫生中需要强大的建模框架.
研究的目的:
- 开发一种机械模型和估计框架,将环境病原体数据与感染个体数量联系起来.
- 在环境监测中应对异质病原体散射的挑战.
- 提供一种使用环境病原体水平估计感染人口的方法.
主要方法:
- 模拟受感染的个体通过波桑过程通过时间变化的速率 (λt) 释放病原体.
- 整合了随机流失曲线,以考虑个人间的变化.
- 开发了一个两步贝叶斯推理框架,用于参数校准和估计.
- 将框架应用于合成数据和在隔离室中的SARS-CoV-2病例研究.
主要成果:
- 该框架将环境病原体水平作为受感染个体,脱落和移除影响的Poisson过程.
- 可识别的模型参数从环境数据中确定.
- 高个体间脱变异导致感染个体的可信度间隔很大.
- 该模型可以区分无感染和低感染水平,以及中度和高感染水平.
结论:
- 开发的框架提供了一种方法,从环境病原体监测数据中估计感染个体的数量.
- 尽管可信度间隔很大,但该模型显示了区分感染水平的潜力,有助于公共卫生应对.
- 考虑到个体间的脱落变化对于准确的环境监测解释至关重要.
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