Using Open-Source Intelligence to Detect Early Signals of COVID-19 in China: Descriptive Study

• DOI: 10.2196/18939
• Publication Date: 2020-06-29T20:03:16Z
• Authors: Elizabeth Benedic...; Xin Chen; Mengyao Zhu; C Raina Macintyre
• Keywords: Original Paper; China; SARS-CoV-2; Pneumonia, Viral; COVID-19; Disclosure; Documentation; Pneumonia; Severe Acute Respiratory Syndrome; Disease Outbreaks; 3. Good health; Coronavirus; Search Engine; Betacoronavirus; 03 medical and health sciences; 0302 clinical medicine; Humans; Public aspects of medicine; RA1-1270; Coronavirus Infections; Pandemics; Retrospective Studies

Background The coronavirus disease (COVID-19) outbreak in China was first reported to the World Health Organization (WHO) on December 31, 2019, and cases were officially identified around 8, 2019. Although origin of COVID-19 has not been confirmed, approximately half early linked a seafood market Wuhan. However, two documented patients did visit market. News reports, social media, informal sources may provide information about outbreaks prior formal notification. Objective aim this study identify signals pneumonia or severe acute respiratory illness (SARI) official recognition 2019 using open-source data. Methods To capture we searched an open source epidemic observatory, EpiWatch, for SARI pneumonia-related illnesses from October 1, searches conducted Google Chinese search engine Baidu. Results There increase reports following notification WHO report that appeared 26, retracted. A November 22, Xiangyang identified, potential index patient retrospectively 17. Conclusions lack with retracted suggests media censorship, given indicate began appearing 8. findings also support relatively recent case 22 transferred Wuhan one incubation period before 8; should be further investigated, as only exposed Another since Hubei 17, suggesting infection present December.