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在传统疾病监测系统做出反应之前识别 COVID-19 的暴发信号。

Identifying the outbreak signal of COVID-19 before the response of the traditional disease monitoring system.

机构信息

Department of Infectious Diseases, Center for Disease Control and Prevention of Nantong City, Nantong, China.

Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.

出版信息

PLoS Negl Trop Dis. 2020 Oct 1;14(10):e0008758. doi: 10.1371/journal.pntd.0008758. eCollection 2020 Oct.

Abstract

Early identification of the emergence of an outbreak of a novel infectious disease is critical to generating a timely response. The traditional monitoring system is adequate for detecting the outbreak of common diseases; however, it is insufficient for the discovery of novel infectious diseases. In this study, we used COVID-19 as an example to compare the delay time of different tools for identifying disease outbreaks. The results showed that both the abnormal spike in influenza-like illnesses and the peak of online searches of key terms could provide early signals. We emphasize the importance of testing these findings and discussing the broader potential to use syndromic surveillance, internet searches, and social media data together with traditional disease surveillance systems for early detection and understanding of novel emerging infectious diseases.

摘要

早期识别新发传染病疫情的出现对于及时做出反应至关重要。传统的监测系统足以检测常见疾病的爆发,但对于新发传染病的发现则不够充分。在本研究中,我们以 COVID-19 为例,比较了不同工具识别疾病爆发的延迟时间。结果表明,流感样疾病的异常激增和关键术语在线搜索的高峰都可以提供早期信号。我们强调了检验这些发现的重要性,并讨论了将综合征监测、互联网搜索和社交媒体数据与传统疾病监测系统结合使用以早期发现和理解新型新发传染病的更广泛潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50de/7553315/00cf9bdd7ec6/pntd.0008758.g001.jpg

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