{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T15:47:49Z","timestamp":1772034469281,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pathogens"],"abstract":"<jats:p>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 (\u00b14.7) 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\u20134756.43, p &lt; 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00\u20130.17, 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.<\/jats:p>","DOI":"10.3390\/pathogens10070783","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T22:10:59Z","timestamp":1624399859000},"page":"783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Digital Biosurveillance for Zoonotic Disease Detection in Kenya"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1041-3696","authenticated-orcid":false,"given":"Ravikiran","family":"Keshavamurthy","sequence":"first","affiliation":[{"name":"Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164, USA"},{"name":"Pacific Northwest National Laboratory, Richland, WA 99354, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5754-0556","authenticated-orcid":false,"given":"Samuel M.","family":"Thumbi","sequence":"additional","affiliation":[{"name":"Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164, USA"},{"name":"Center for Epidemiological Modelling and Analysis, Institute of Tropical and Infectious Diseases, University of Nairobi, Nairobi 30197, Kenya"},{"name":"Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh EH9 3FL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0727-0479","authenticated-orcid":false,"given":"Lauren E.","family":"Charles","sequence":"additional","affiliation":[{"name":"Paul G. 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