Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link
Volume 31, Number 2—February 2025
Research

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

Damian Honeyman, Deepti Gurdasani, Adriana Notaras, Zubair Akhtar, Jared Edgeworth, Aye Moa, Abrar Ahmad Chughtai, Ashley Quigley, Samsung Lim, and Chandini Raina MacIntyreComments to Author 
Author affiliation: The University of New South Wales, Sydney, New South Wales, Australia (D. Honeyman, D. Gurdasani, A. Notaras, Z. Akhtar, J. Edgeworth, A. Moa, A.A. Chughtai, A. Quigley, S. Lim, C.R. MacIntyre); University of Western Australia, Perth, Western Australia, Australia (D. Gurdasani); Queen Mary University of London, London, UK (D. Gurdasani); Arizona State University, Tempe, Arizona, USA (C.R. MacIntyre)

Main Article

Table 7

Proportion of syndromes diagnosed across high and lower-middle and upper-middle–income economies identified during analysis of global epidemiology of outbreaks of unknown cause identified by open-source intelligence, 2020–2022*

Cause Income, no. (%)
 Total
High LMIE/UMIE
Unknown 29 (60) 235 (89) 264 (85)
Known 19 (40) 28 (11) 47 (15
Total 48 (100) 263 (100 311 (100)

*Pearson χ2(1) = 26.49; p<0.0001. LMIE, low-middle-income economies; UMIE, upper-middle-income economies.

Main Article

Page created: January 15, 2025
Page updated: January 31, 2025
Page reviewed: January 31, 2025
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
file_external