基于贝叶斯模型的最优传染病检测方法
Optimal Infectious Disease Detection Method Based on Bayesian Model
刘文君 1邹国华 1鲍勤 2马立盟1
作者信息
- 1. 首都师范大学数学科学学院,北京 100048
- 2. 中国科学院数学与系统科学研究院,北京 100190
- 折叠
摘要
随着全球传染病病毒变种的不断涌现以及生物安全形势的日益严峻,传染病检测的准确性和检测效率日益成为及时监控病毒传播、保护公众健康的关键环节.然而,由于技术限制和操作规范性等因素的影响,检测的可靠性存在一定的挑战.本研究针对传染病大规模检测的准确性问题展开探讨,以新冠疫情为例,基于贝叶斯统计模型计算了核酸"单检"和"混检"的假阴性预测值,并提出了针对不同风险地区和敏感性的最优检测策略,包括"单检"与"混检"策略的选择、检测人数和频率的优化、定期检测间隔的调整;另外,针对低风险地区的男女分轮次检测方案,计算了相应的检测精度与检测最优间隔时间.最后,结合本研究的测算结果,我们给出了有关传染病大规模检测的政策建议,旨在为传染病的有效防控提供科学依据和政策支持.
Abstract
With the ongoing emergence of global infectious disease virus variants and the increasingly severe biological security situation,the accuracy and efficiency of infectious disease detection have become crucial components in monitoring virus spread and protecting public health.However,the reliability of detection is often challenged by factors such as technical limitations and operational standardization.This paper examines the accuracy of large-scale detection of infectious diseases.Tak-ing the COVID-19 epidemic as an example,we calculated the false-negative predictive values of"single test"and"pooled test"based on Bayesian statistical modeling,and proposed optimal testing strategies for different areas and sensitivities.It includes the selection of"single testing"and"pooled testing"strategies,the optimization of testing population and frequency,and adjustments to the intervals of regular testing.Furthermore,we computed the corresponding testing accuracy and optimal interval times for gender-separated testing schemes in low-risk areas.Finally,combined with the calculation results of this study,we formulated policy recommendations concern-ing infectious disease detection,aimed at providing a scientific foundation and policy support for effective disease prevention and control.
关键词
传染病检测/贝叶斯模型/核酸检测/假阴性预测Key words
infectious disease detection/Bayesian model/nucleic acid testing/false negative prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据