抗微生物耐药性传播模型的被忽视的模型验证 - - 一个系统的审查
Maja L Brinch1, Andrea Palladino2, Jeroen Geurtsen3
1Risk-Benefit, DTU National Food Institute, Kgs. Lyngby, Denmark. malbri@food.dtu.dk.
Antimicrobial resistance and infection control
|May 28, 2025
概括
数学传播模型对于针对抗微生物药物耐药性的公共卫生干预至关重要. 然而,在范围,文档和验证方面的持续差距阻碍了它们的有效性和更新. 未来的研究必须优先考虑可靠的实践和新的验证数据.
科学领域:
- 数学建模的数学建模
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 数学传播模型是指导针对抗菌素耐药性的公共卫生干预的重要工具.
- 之前的研究已经确定了需要进一步调查的建模差距.
研究的目的:
- 审查抗微生物耐药性传播研究中持续存在的建模差距.
- 扩大范围,包括新的控制措施,如单克隆抗体,并评估二次感染的影响.
- 通过分析模型开发和文档来指导未来的建模工作.
主要方法:
- 按照PRISMA指南进行的系统审查.
- 识别和描述模型重点,动态和报告中的差距.
- 应用TRACE范式来评估在选定的研究中的模型开发和文档.
主要成果:
- 分析了170个传播研究 (2010-2022年),其中Mycobacterium结核病和Staphylococcus aureus耐药性是最常见的模型.
- 检查多种干预措施 (药物治疗,疫苗接种) 的研究趋势越来越强.
- 基于人口的分组模型占主导地位;然而,TRACE框架应用程序显示,许多研究中缺乏软件测试,验证和外部数据验证.
结论:
- 在抗微生物药物耐药性模型的范围,地理覆盖,药物-病原体组合和病毒-细菌动态方面仍然存在重大差距.
- 不足够的文档阻碍了模型更新和一致的政策建议.
- 强有力的建模实践至关重要,包括将新的数据纳入验证,以完善模型并确保决策者获得可靠的结果.
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