Digital Biosurveillance for Zoonotic Disease Detection in Kenya
Abstract
Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01–4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00–0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.
- Authors:
-
[1];
[2];
[1]
- Washington State Univ., Pullman, WA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Washington State Univ., Pullman, WA (United States); Univ. of Nairobi (Kenya); Univ. of Edinburgh, Scotland (United Kingdom)
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1809029
- Report Number(s):
- PNNL-SA-162746
Journal ID: ISSN 2076-0817
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Pathogens
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 7; Journal ID: ISSN 2076-0817
- Publisher:
- MDPI
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; zoonosis; biosurveillance; digital surveillance; open-source information; disease taxonomy; Kenya
Citation Formats
Keshavamurthy, Ravikiran, Thumbi, Samuel M., and Charles, Lauren E. Digital Biosurveillance for Zoonotic Disease Detection in Kenya. United States: N. p., 2021.
Web. doi:10.3390/pathogens10070783.
Keshavamurthy, Ravikiran, Thumbi, Samuel M., & Charles, Lauren E. Digital Biosurveillance for Zoonotic Disease Detection in Kenya. United States. https://doi.org/10.3390/pathogens10070783
Keshavamurthy, Ravikiran, Thumbi, Samuel M., and Charles, Lauren E. Tue .
"Digital Biosurveillance for Zoonotic Disease Detection in Kenya". United States. https://doi.org/10.3390/pathogens10070783. https://www.osti.gov/servlets/purl/1809029.
@article{osti_1809029,
title = {Digital Biosurveillance for Zoonotic Disease Detection in Kenya},
author = {Keshavamurthy, Ravikiran and Thumbi, Samuel M. and Charles, Lauren E.},
abstractNote = {Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01–4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00–0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.},
doi = {10.3390/pathogens10070783},
journal = {Pathogens},
number = 7,
volume = 10,
place = {United States},
year = {Tue Jun 22 00:00:00 EDT 2021},
month = {Tue Jun 22 00:00:00 EDT 2021}
}
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