Global Epidemiology of Outbreaks of Unknown Cause Identified by Open-Source Intelligence, 2020–2022

Abstract

Epidemic surveillance using traditional approaches is dependent on case ascertainment and is delayed. Open-source intelligence (OSINT)–based syndromic surveillance can overcome limitations of delayed surveillance and poor case ascertainment, providing early warnings to guide outbreak response. It can identify outbreaks of unknown cause for which no other global surveillance exists. Using the artificial intelligence–based OSINT early warning system EPIWATCH, we describe the global epidemiology of 310 outbreaks of unknown cause that occurred December 31, 2019–January 1, 2023. The outbreaks were associated with 75,968 reported human cases and 4,235 deaths. We identified where OSINT signaled outbreaks earlier than official sources and before diagnoses were made. We identified possible signals of known disease outbreaks with poor case ascertainment. A cause was subsequently reported for only 14% of outbreaks analyzed; the percentage was substantially lower in lower/upper-middle–income economies than high-income economies, highlighting the utility of OSINT-based syndromic surveillance for early warnings, particularly in resource-poor settings.