近日，我院助理教授魏璐、管理科学系2021届硕士研究生李小婧、荆中博副教授、刘志东教授合作的学术论文“A novel textual track-data-based approach for estimating individual infection risk of COVID-19”在风险管理领域顶级期刊Risk Analysis(SCI/SSCI JCR Q1)发表。（论文在线链接：https://onlinelibrary.wiley.com/doi/10.1111/risa.13944）
Risk Analysis是风险分析协会（SRA）的官方期刊，英国商学院协会（the Association of Business School,简称ABS）出版的高质量学术期刊指南（ABS Academic Journal Quality Guide）中的ABS四星期刊，FMS管理科学高质量期刊A级国际期刊。
论文将新冠肺炎确诊患者的轨迹文本数据应用于度量个体感染COVID-19风险的高低，从地点、地区、密切接触者、接触方式、出行方式和症状等六大类特征刻画新冠肺炎患者画像，构造了新冠肺炎个体感染风险度量指标，对于识别高风险人群，控制疫情的发展具有重要意义。
论文摘要：
With the recurrence of infectious diseases caused by coronaviruses, which pose a significant threat to human health, there is an unprecedented urgency to devise an effective method to identify and assess who is most at risk of contracting these diseases. China has successfully controlled the spread of COVID-19 through the disclosure of track data belonging to diagnosed patients. This paper proposes a novel textual track-data-based approach for individual infection risk measurement. The proposed approach is divided into three steps. First, track features are extracted from track data to build a general portrait of COVID-19 patients. Then, based on the extracted track features, we construct an infection risk indicator system to calculate the infection risk index (IRI). Finally, individuals are divided into different infection risk categories based on the IRI values. By doing so, the proposed approach can determine the risk of an individual contracting COVID-19, which facilitates the identification of high-risk populations. Thus, the proposed approach can be used for risk prevention and control of COVID-19. In the empirical analysis, we comprehensively collected 9,455 pieces of track data from 20 January 2020 to 30 July 2020, covering 32 provinces/provincial municipalities in China. The empirical results show that the Chinese COVID-19 patients have six key features that indicate infection risk: place, region, close-contact person, contact manner, travel mode, and symptom. The IRI values for all 9,455 patients vary from 0 to 43.19. Individuals are classified into five infection risk categories: low, moderate-low, moderate, moderate-high, and high risk.
冠状病毒引起的传染病对人类健康构成重大威胁，随着这些传染病的一再爆发，迫切需要构建一种有效的方法来识别感染疾病的高风险人群。中国通过披露确诊患者的轨迹数据，成功控制了新冠病毒的传播。有鉴于此，本文提出了一种全新的基于文本轨迹数据的个体感染风险度量方法。该方法具体分为三个步骤。首先，从轨迹数据中提取轨迹特征，构建新冠肺炎患者的画像。然后，基于提取的轨迹特征，构建感染风险指标体系，计算感染风险指数（IRI）。最后，根据IRI值将个体分为不同的感染风险类别。通过这样做，本文提出的方法可以量化个体感染新冠肺炎的风险高低，这对于识别高危人群非常有价值。因此，该方法可用于新冠肺炎的风险预防与控制。在实证分析中，我们收集了2020年1月20日至2020年7月30日中国32个省/直辖市共9455条患者轨迹数据。实证结果表明，中国的新冠肺炎患者具有六个关键特征：地点、地区、密切接触者、接触方式、出行方式和症状。9455名患者的IRI值从0到43.19不等。人群可被分为五个感染风险类别：低、中-低、中、中-高和高风险。
撰稿：魏璐
审核：刘志东
