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

Abstract

New coronavirus cases and related deaths are continuing to occur worldwide. Early identification of the emergence of novel outbreaks of infectious diseases is critical to the generation of timely responses. We performed a comparative study to determine the feasibility of the early detection of the COVID-19 outbreak in China based on influenza surveillance data and the internet-based Baidu search index to evaluate the timelines of the alert signals compared with the traditional case reporting and response systems. An abnormal increase in the number of influenza-like illnesses (ILI) occurred at least one month earlier than the clinical reports of pneumonia with unknown causes and the conventional monitoring system. The peak of the search volume was 20 days earlier than the issuance of the massive official warning about the epidemic. The findings from this study suggest that monitoring abnormal surges of ILI and identifying peaks of online searches of key terms can provide early signals of novel disease outbreaks. We emphasize the importance of broadening the potential of syndromic surveillance, internet searches, and social media data together with the traditional disease surveillance system to enhance early detection and understanding of emerging infectious diseases. Synopsis 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.

Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities

“…We focused on seven key events (events I-VII) within the period from December 31, 2019, when the Wuhan Health Commission announced a possible outbreak of an unknown new pneumonia, to March 18, 2020, one week after the World Health Organization (WHO) declared a pandemic and local pandemic was under control throughout China ( Fig. 2 a) [ 3 , 21 ]. After the Wuhan Health Commission announcement (event I), national-level public attention remained steady at the outset and increased rapidly after two weeks when human-to-human transmission of an unknown new pneumonia was confirmed (event II).…”

“…Internet search engine products, such as Google Trends and Baidu Index, have been widely used as indicators to illustrate the collective public attention to COVID-19 [ [18] , [19] , [20] ]. In China, infoveillance studies based on the Baidu Index identified two critical periods for early-stage disease control when national public attention was low [ 9 , 21 ]. In the United States, infoveillance studies using Google Trends showed that the initial public attention to COVID-19 at the national level was limited, short-lived, and even decreased during the critical period when increasingly strong control strategies were put in place [ 13 , 22 ].…”

“…Using Google Trends, similar findings reflective of relatively low-level public attention were also reported globally in both early infected developed and developing countries [ 9 , 10 , 12 ]. However, most COVID-19 infodemiology studies using internet search engine data are currently conducted at the national level [ 9 , 10 , 13 , 21 , 22 ].…”

Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets

“…Previous studies have demonstrated the potential to apply analytical models to identify potential outbreaks in response to other diseases [18]. However, these methods rely on large, complex data sets extracted from a specific health system or region [19][20][21].…”

Progression of the Pathway for Public Health Care during the COVID-19 Outbreak at District Health Office

“…Outbreak preparedness is the first essential step, and this includes outbreak protocols, designation of coordinators and teams, and the assembly of the necessary materials for outbreak investigation and response, as well as identifying training needs that should be addressed well before the occurrence of an outbreak. A good surveillance system would allow early detection of outbreaks, leading to activities related to outbreak investigation and response [ 12 , 13 , 14 ].…”