dc.contributor.author | Keshavamurthy, Ravikiran | |
dc.contributor.author | Thumbi, Samuel M | |
dc.contributor.author | Lauren, E Charles | |
dc.date.accessioned | 2021-09-03T09:39:12Z | |
dc.date.available | 2021-09-03T09:39:12Z | |
dc.date.issued | 2021-06 | |
dc.identifier.citation | Keshavamurthy R, Thumbi SM, Charles LE. Digital Biosurveillance for Zoonotic Disease Detection in Kenya. Pathogens. 2021 Jun 22;10(7):783. doi: 10.3390/pathogens10070783. PMID: 34206236; PMCID: PMC8308926. | en_US |
dc.identifier.uri | https://pubmed.ncbi.nlm.nih.gov/34206236/ | |
dc.identifier.uri | http://erepository.uonbi.ac.ke/handle/11295/155417 | |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Nairobi | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Kenya; biosurveillance; digital surveillance; disease taxonomy; open-source information; zoonosis. | en_US |
dc.title | Digital Biosurveillance for Zoonotic Disease Detection in Kenya | en_US |
dc.type | Article | en_US |