一种创新的方法用于预测复杂的废水网络中的流行病热点,使用图形理论与模糊逻辑相结合
Puru Dutt Sharma1, Srinivas Rallapalli1,2, Naga Rajiv Lakkaniga3
1Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan India.
概括
这项研究引入了一种结合图形网络和模糊逻辑的新方法,用于在废水中预测早期的COVID-19热点. 该方法增强了针对性公共卫生干预的基于废水的流行病学.
科学领域:
- 环境微生物学环境微生物学
- 流行病学 流行病学
- 计算生物学是一种计算生物学.
背景情况:
- 早期检测COVID-19热点对于控制病毒传播至关重要.
- 诊断测试往往太慢,无法快速进行人口层面的监测.
- 基于废水的流行病学 (WBE) 显示了SARS-CoV-2 RNA检测的希望.
研究的目的:
- 开发一种系统和有针对性的方法,用于在废水网络中跟踪COVID-19.
- 用图形网络理论和模糊逻辑预测社区内潜在的COVID-19热点.
- 加强传统的WBE方法,以实现更快,更准确的热点识别.
主要方法:
- 整合图形网络 (GN) 理论,将废水网络划分为社区.
- 应用基于模糊逻辑的推断系统来识别测试的目标社区.
- 分析了美国明尼苏达州4000例病例样本,考虑了各种因素.
- 使用模糊的会员函数将不确定性纳入COVID-19的贡献因素.
主要成果:
- 为热点预测开发了一种结合图形理论和模糊逻辑的新方法.
- 42%的分析病例被确定为潜在的COVID-19热点,概率得分>0.8.8.
- 该方法为废水网络中确定的热点提供了一个概率百分比.
- 该方法成功地结合了与促成因素相关的不确定性.
结论:
- 集成的GN和模糊逻辑方法为WBE提供了显著的增强.
- 这一有针对性的战略使得在潜在的热点地区能够进行快速测试和高效的疫苗接种活动.
- 该方法改善了对未来流行病的准备和变种传播管理.
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