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

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.

Fig 1. Comparison of the COVID-19 outbreak and Baidu searching index.

A. Timeline of the COVID-19 outbreak and official response. Only confirmed cases were analyzed referring to the report by The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team in China. [10] B. The online search index for the terms of “pneumonia” and “SARS”.

Fig 2

Reported influenza-like illness (ILI) cases during 2015–2019 A. The long-term trend of monthly reported ILI cases. B. Comparison of monthly reported ILI cases in different years.