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Identification of COVID-19 Outbreak Signals Prior to the Traditional Disease Surveillance System
16 Pages Posted: 8 May 2020
More...Abstract
Background: New coronavirus cases and deaths are continuing to pop up worldwide. Early identifying the emergence of a novel infectious disease outbreak is of great value for a timely response.
Methods: We performed a comparative study to discuss the feasibility of early warning of the outbreak of novel coronavirus (COVID-19) based on the influenza surveillance data and internet search index of keywords in order to evaluate the early warning capability and timelines of the alert signals in comparison with the traditional case reporting system and official response in China.
Findings: Influenza cases notified in 2019 was significantly higher in comparison to previous years in China. An early spike of influenza was observed in 2019 winter, with a fast-growing period from November to December, suggesting that the COVID-19 cases may occur during October and November or earlier. There was a surge in searching for information related to “pneumonia” and “SARS” on 31 December 2019. However, according to the traditional official surveillance system, the risk of the potential epidemic was not taken seriously since the first group of unexplained pneumonia cases reported in Wuhan on 29 December, 2019, till 20 January, 2020. Both the unusual increase of influenza and the peak of internet search for key terms have shown earlier signals of the outbreak of COVID-19.
Interpretation: The traditional disease monitoring system is effective to detect the outbreak of common infectious diseases, but it is insufficient for the discovery of novel diseases. Monitoring abnormal spike of influenza-like illness and identifying online search peak of key terms can provide early signals of a novel disease outbreak.
Funding Statement: This study was funded by the National Natural Science Foundation of China (81973103), National Key R&D Program of China (2017YFC0907000), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Declaration of Interests: The authors declare no conflict of interest.
Ethics Approval Statement: This study was approved by the ethics committee of Nanjing Medical University.
Keywords: COVID-19; detection; coronavirus; influenza; internet search
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