数字联系人追踪中的流行病暴露风险评估:一种模糊逻辑方法
Mohsen Rashidian1, Mohammad Reza Malek1, Abolghasem Sadeghi-Niaraki2
1Ubiquitous and Mobile GIS Research Lab., Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran Iran.
Digital health
|July 26, 2024
概括
这项研究引入了用于数字联系人追踪的新模糊逻辑框架,改进了超越蓝牙低能耗 (BLE) 方法的暴露风险评估. 该系统提高了对各种疾病和变种的准确性和适应性.
科学领域:
- 流行病学 流行病学
- 公共卫生技术 公共卫生技术
- 计算智能是一种计算智能.
背景情况:
- 蓝牙低能 (BLE) 接触追踪在COVID-19期间至关重要,但由于依赖信号强度而受到限制.
- 现有的BLE方法在适应新病毒变种和适应未来流行病的可扩展性方面扎.
研究的目的:
- 开发一种用于评估个人感染传染病暴露风险的新框架.
- 通过使用模糊专家系统,增强数字联系人追踪的适应性和可扩展性.
主要方法:
- 采用了一个模糊的专家系统,整合了接触特征 (距离,持续时间) 和环境因素 (人群规模,感染率).
- 该框架使用两个不同的病毒进行了测试,采用了四个会员功能和256个模糊规则.
主要成果:
- 该系统准确地将暴露风险分为四个级别:低,中,高和过高.
- 经验结果表明,与BLE-only方法相比,经验结果的准确性和精度更高,虚假阳性结果减少.
结论:
- 拟议的框架为真实世界的暴露风险评估提供了一种实用且可适应的解决方案.
- 这种方法有可能为各种各样的流行病情景开发更有效,更可扩展的数字联系追踪应用程序.
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