Respiratory illness of unknown cause as an early signal of the COVID-19 pandemic

Abstract

Background: As the COVID-19 pandemic began, cases of respiratory illness of unknown cause could have presented early disease signals. The detection of such signals can provide useful clues regarding disease emergence and opportunities for early outbreak control. This study assessed trends in the global incidence of reports of respiratory illness of unknown cause as early indicators of COVID-19 in late 2019, using the early warning system, EPIWATCH, to investigate the disease’s origins and early epidemiology. Methods: Global news articles published between 1-Oct and 31-Dec in 2019 and collected by EPIWATCH, an open-source Artificial Intelligence (AI)-driven epidemic surveillance system, were searched for keywords relating to unknown respiratory illness. Control periods representing the pre-COVID period (1-Oct to 31-Dec 2016-2018) were used to compare report trends over time. Data on report location, case demographics, outbreak setting, and type of illness, were extracted and descriptively analysed. Fisher’s exact tests were conducted to assess proportional differences between years. Results: Overall, 113 EPIWATCH reports of unknown respiratory illness were identified from 12 countries in 2019. Most reports (n=111 [98.2%]) were published in Northern hemisphere countries, in particular Russia (n=A72 [63.7%]) and the United States (n=17 [15.0%]). China experienced a month-on-month increase in reports. Schools were the most reported outbreak setting (n=49 [43.4%]). The proportion of included reports in 2019 was significantly higher compared to 2016-2018 (p<.001). Conclusion: Potential early COVID-19 signals were detected from various countries before the end of 2019. AI-driven open-source disease surveillance systems, such as EPIWATCH, can provide early warnings to enhance disease surveillance and improve outbreak prevention and response.