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基于情景模拟的新型冠状病毒肺炎密切接触者感染风险评估

Risk assessment of infection of COVID-19 contacts based on scenario simulation.

作者信息

Zhang Wei-Wen, Huang Yan-Ran, Wang Yu-Yuan, Lu Ze-Xi, Sun Jia-Lin, Jing Ming-Xia

机构信息

Department of Preventive Medicine, Shihezi University School of Medicine, Shihezi, China.

Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Xinjiang, China.

出版信息

Risk Anal. 2025 Feb;45(2):322-341. doi: 10.1111/risa.15103. Epub 2024 Jul 29.

Abstract

We constructed a rapid infection risk assessment model for contacts of COVID-19. The improved Wells-Riley model was used to estimate the probability of infection for contacts of COVID-19 in the same place and evaluate their risk grades. We used COVID-19 outbreaks that were documented to validate the accuracy of the model. We analyzed the relationship between controllable factors and infection probability and constructed common scenarios to analyze the infection risk of contacts in different scenarios. The model showed the robustness of the fitting (mean relative error = 5.89%, mean absolute error = 2.03%, root mean squared error = 2.03%, R = 0.991). We found that improving ventilation from poorly ventilated to naturally ventilated and wearing masks can reduce the probability of infection by about two times. Contacts in places of light activity, loud talking or singing, and heavy exercise, oral breathing (e.g., gyms, KTV, choirs) were at higher risk of infection. The model constructed in this study can quickly and accurately assess the infection risk grades of COVID-19 contacts. Simply opening doors and windows for ventilation can significantly reduce the risk of infection in certain places. The places of light activity, loud talking or singing, and heavy exercise, oral breathing, should pay more attention to prevent and control transmission of the epidemic.

摘要

我们构建了一个针对新型冠状病毒肺炎(COVID-19)密切接触者的快速感染风险评估模型。采用改进的威尔斯-莱利模型来估计在同一地点的COVID-19密切接触者的感染概率,并评估其风险等级。我们使用已记录的COVID-19疫情来验证该模型的准确性。我们分析了可控因素与感染概率之间的关系,并构建常见场景来分析不同场景下密切接触者的感染风险。该模型显示出良好的拟合稳健性(平均相对误差=5.89%,平均绝对误差=2.03%,均方根误差=2.03%,R=0.991)。我们发现,将通风条件从通风不良改善为自然通风以及佩戴口罩可使感染概率降低约两倍。在轻度活动、大声交谈或唱歌、剧烈运动、张口呼吸的场所(如健身房、KTV、合唱团)的密切接触者感染风险较高。本研究构建的模型能够快速、准确地评估COVID-19密切接触者的感染风险等级。简单地打开门窗通风可显著降低某些场所的感染风险。在轻度活动、大声交谈或唱歌、剧烈运动、张口呼吸的场所应更加重视疫情防控传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b687/11787960/fa1ba1e31709/RISA-45-322-g001.jpg

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