Journal article
Global Epidemiology of Outbreaks of Unknown Cause Identified by Open-Source Intelligence, 2020–2022
Emerging infectious diseases, Vol.31(2), pp.298-308
02/2025
PMID: 39983687
Appears in Recent Faculty of Health Publications
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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.
Details
- Title
- Global Epidemiology of Outbreaks of Unknown Cause Identified by Open-Source Intelligence, 2020–2022
- Creators
- Damian Honeyman - University of New South Wales (Australia, Sydney)Deepti Gurdasani - Queen Mary University of LondonAdriana Notaras - University of New South Wales (Australia, Sydney)Zubair Akhtar - University of New South Wales (Australia, Sydney)Jared Edgeworth - University of New South Wales (Australia, Sydney)Aye Moa - University of New South Wales (Australia, Sydney)Abrar Ahmad Chughtai - University of New South Wales (Australia, Sydney)Ashley Quigley - University of New South Wales (Australia, Sydney)Samsung Lim - University of New South Wales (Australia, Sydney)Chandini Raina MacIntyre - Arizona State University
- Publication Details
- Emerging infectious diseases, Vol.31(2), pp.298-308
- Publisher
- CDC
- Number of pages
- 11
- Identifiers
- 991013376451302368
- Academic Unit
- Faculty of Health
- Language
- English
- Resource Type
- Journal article