基于COVID-19人群的接触者追踪网络的个性化风险评分预测与测试策略适应性

IF 2.2 4区 医学 Q3 INFECTIOUS DISEASES
Shushan Wu, Yan Feng, Huimin Cheng, Hui Huang, Yang Li, Feng Ling, Ping Ma, Wenxuan Zhong, Ye Shen
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引用次数: 0

摘要

接触者追踪是控制疫情快速传播的有效公共卫生政策。政府对确诊SARS-CoV-2病例的接触者进行追踪,建议进行检测,鼓励自我隔离,并监测接触者的症状。在发展中国家和欠发达国家,用于广泛检测SARS-CoV-2的资源有限,确定和隔离阳性接触者以控制疫情仍然至关重要。因此,在为这些触点实施测试策略时,分析召回率和精确度是必要的。我们分析了来自中国东部某省2020年1月至2020年7月期间感染SARS-CoV-2的827名指数患者及其14814名密切接触者的接触者追踪数据集。我们从数据中构建了一个网络,并使用图卷积网络来预测每个接触者的感染状态。据我们所知,这是第一个使用基于人群的接触者追踪数据来使用图神经网络预测感染状态的方法。尽管信息有限,但与医院发病情景相比,我们的模型实现了接受者工作特征曲线下的竞争性区域(ROC AUC)。基于风险评分,我们提出了几种接触检测政策调整,以平衡资源效率和有效的大流行控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Personalized risk score prediction and testing policy adaptations of a COVID-19 population-based contact tracing network.

Personalized risk score prediction and testing policy adaptations of a COVID-19 population-based contact tracing network.

Personalized risk score prediction and testing policy adaptations of a COVID-19 population-based contact tracing network.

Personalized risk score prediction and testing policy adaptations of a COVID-19 population-based contact tracing network.

Contact tracing is an effective public health policy to put the fast-spreading epidemic under control. The government tracks the contacts of confirmed SARS-CoV-2 cases, recommends testing, encourages self-quarantine, and monitors symptoms of contacts. In developing and less-developed countries with limited resources for widespread SARS-CoV-2 testing, it remains essential to identify and quarantine positive contacts to control outbreaks. Therefore, analysing recall and precision when implementing testing policies for these contacts is necessary. We analysed a contact tracing dataset from a cohort of 827 index patients infected with SARS-CoV-2 and their 14814 close contacts from Jan 2020 to July 2020 in a province in eastern China. We constructed a network from the data and used a Graph Convolutional Network to predict each contact's infection status. To the best of our knowledge, this is the first method to use population-based contact tracing data for predicting the infection status using graph neural networks. Despite limited information, our model achieves competitive Area Under the Receiver Operating Characteristic Curve (ROC AUC) compared to hospital-onset scenarios. Based on the risk scores, we propose several contact testing policy adaptations that balance resource efficiency and effective pandemic control.

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来源期刊
Epidemiology and Infection
Epidemiology and Infection 医学-传染病学
CiteScore
4.10
自引率
2.40%
发文量
366
审稿时长
3-6 weeks
期刊介绍: Epidemiology & Infection publishes original reports and reviews on all aspects of infection in humans and animals. Particular emphasis is given to the epidemiology, prevention and control of infectious diseases. The scope covers the zoonoses, outbreaks, food hygiene, vaccine studies, statistics and the clinical, social and public-health aspects of infectious disease, as well as some tropical infections. It has become the key international periodical in which to find the latest reports on recently discovered infections and new technology. For those concerned with policy and planning for the control of infections, the papers on mathematical modelling of epidemics caused by historical, current and emergent infections are of particular value.
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