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. 2019 Dec:89:146-153.
doi: 10.1016/j.ijid.2019.10.011. Epub 2019 Oct 16.

Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack?

Jennifer Wilburn  1 , Catherine O'Connor  2 , Amanda L Walsh  2 , Dilys Morgan  2
Affiliations

Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack?

Jennifer Wilburn et al. Int J Infect Dis. 2019 Dec.

Abstract

Background: Epidemic intelligence (EI) for emerging infections is the process of identifying key information on emerging infectious diseases and specific incidents. Automated web-based infectious disease surveillance technologies are available; however, human input is still needed to review, validate, and interpret these sources. In this study, entries captured by Public Health England's (PHE) manual event-based EI system were examined to inform future intelligence gathering activities.

Methods: A descriptive analysis of unique events captured in a database between 2013 and 2017 was conducted. The top five diseases in terms of the number of entries were described in depth to determine the effectiveness of PHE's EI surveillance system compared to other sources.

Results: Between 2013 and 2017, a total of 22 847 unique entries were added to the database. The top three initial and definitive information sources varied considerably by disease. Ebola entries dominated the database, making up 23.7% of the total, followed by Zika (11.8%), Middle East respiratory syndrome (6.7%), cholera (5.5%), and yellow fever and undiagnosed morbidity (both 3.3%). Initial reports of major outbreaks due to the top five disease agents were picked up through the manual system prior to being publicly reported by official sources.

Conclusions: PHE's manual EI process quickly and accurately detected global public health threats at the earliest stages and allowed for monitoring of events as they evolved.

Keywords: Disease surveillance; Early detection; Epidemic intelligence; Epidemiology; Surveillance systems.

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Figures

Figure 1
Figure 1
Heat map of database entries by world region. The heat map created in ArcGIS shows the distribution of database entries by world region using the United Nations geoscheme.
Figure 2
Figure 2
Distribution of Ebola entries by month and year. A graphical depiction of the distribution of Ebola entries captured by PHE’s epidemic intelligence system over the study period. Important events are noted through the use of text boxes and arrows.
Figure 3
Figure 3
Distribution of Zika entries by month and year. A graphical depiction of the distribution of Zika entries captured by PHE’s epidemic intelligence system over the study period. Important events are noted through the use of text boxes and arrows.
Figure 4
Figure 4
Distribution of Middle East respiratory syndrome (MERS) entries by month and year. A graphical depiction of the distribution of MERS entries captured by PHE’s epidemic intelligence system over the study period. Important events are noted through the use of text boxes and arrows.
Figure 5
Figure 5
Distribution of cholera entries by month and year. A graphical depiction of the distribution of cholera entries captured by PHE’s epidemic intelligence system over the study period. Important events are noted through the use of text boxes and arrows.
Figure 6
Figure 6
Distribution of yellow fever entries by month and year. A graphical depiction of the distribution of yellow fever entries captured by PHE’s epidemic intelligence system over the study period. Important events are noted through the use of text boxes and arrows.
See this image and copyright information in PMC

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