Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis
Brucellosis is a bacterial zoonotic disease which poses a significant public health challenge globally. In Kenya, it is a priority zoonosis, causing morbidity and losses in humans and animals. Here, we used monthly surveillance data from 2014 to 2022 from the official human and animal health surveil...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/178734 |
| _version_ | 1855533696057081856 |
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| author | Kahariri, Samuel Thomas, Lian F. Bett, Bernard K. Mureithi, M.W. Makori, A. Njuguna, B. Kadivane, S. Makau, D.N. Mutono, N. Thumbi, S.M. |
| author_browse | Bett, Bernard K. Kadivane, S. Kahariri, Samuel Makau, D.N. Makori, A. Mureithi, M.W. Mutono, N. Njuguna, B. Thomas, Lian F. Thumbi, S.M. |
| author_facet | Kahariri, Samuel Thomas, Lian F. Bett, Bernard K. Mureithi, M.W. Makori, A. Njuguna, B. Kadivane, S. Makau, D.N. Mutono, N. Thumbi, S.M. |
| author_sort | Kahariri, Samuel |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Brucellosis is a bacterial zoonotic disease which poses a significant public health challenge globally. In Kenya, it is a priority zoonosis, causing morbidity and losses in humans and animals. Here, we used monthly surveillance data from 2014 to 2022 from the official human and animal health surveillance databases. We conducted spatiotemporal analysis, tested associations between human and animal brucellosis using Time Series Linear Models, and forecasted the incidence of human brucellosis for twelve months using Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Our analysis revealed a significant disparity in brucellosis cases, with a much higher cumulative number of human cases (4,688,787) compared to animal cases (1214). Human incidence depicted a relatively stable trend, with occasional fluctuations. However, cattle and camel incidences displayed sporadic peaks and troughs. Only cattle brucellosis was significantly associated (estimate: 0.355; 95% CI: 0.004 to 0.707) with human brucellosis. SARIMA models demonstrated reasonable predictive accuracy for human incidence, but incorporating animal data did not significantly improve model performance. Our study highlights the weaknesses in the existing surveillance systems and the need for comprehensive evaluation of the systems and implementation of integrated surveillance to address gaps in surveillance, improve the accuracy of predictive analysis, and enhance early detection for zoonotic diseases. |
| format | Journal Article |
| id | CGSpace178734 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1787342025-12-11T12:07:30Z Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis Kahariri, Samuel Thomas, Lian F. Bett, Bernard K. Mureithi, M.W. Makori, A. Njuguna, B. Kadivane, S. Makau, D.N. Mutono, N. Thumbi, S.M. animal health brucellosis disease control health zoonoses Brucellosis is a bacterial zoonotic disease which poses a significant public health challenge globally. In Kenya, it is a priority zoonosis, causing morbidity and losses in humans and animals. Here, we used monthly surveillance data from 2014 to 2022 from the official human and animal health surveillance databases. We conducted spatiotemporal analysis, tested associations between human and animal brucellosis using Time Series Linear Models, and forecasted the incidence of human brucellosis for twelve months using Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Our analysis revealed a significant disparity in brucellosis cases, with a much higher cumulative number of human cases (4,688,787) compared to animal cases (1214). Human incidence depicted a relatively stable trend, with occasional fluctuations. However, cattle and camel incidences displayed sporadic peaks and troughs. Only cattle brucellosis was significantly associated (estimate: 0.355; 95% CI: 0.004 to 0.707) with human brucellosis. SARIMA models demonstrated reasonable predictive accuracy for human incidence, but incorporating animal data did not significantly improve model performance. Our study highlights the weaknesses in the existing surveillance systems and the need for comprehensive evaluation of the systems and implementation of integrated surveillance to address gaps in surveillance, improve the accuracy of predictive analysis, and enhance early detection for zoonotic diseases. 2025-12-09 2025-12-11T09:24:28Z 2025-12-11T09:24:28Z Journal Article https://hdl.handle.net/10568/178734 en Open Access MDPI Kahariri, S., Thomas, L.F., Bett, B., Mureithi, M.W., Makori, A., Njuguna, B., Kadivane, S., Makau, D.N., Mutono, N. and Thumbi, S.M. 2025. Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis. Tropical Medicine and Infectious Disease 10 (12): 344. |
| spellingShingle | animal health brucellosis disease control health zoonoses Kahariri, Samuel Thomas, Lian F. Bett, Bernard K. Mureithi, M.W. Makori, A. Njuguna, B. Kadivane, S. Makau, D.N. Mutono, N. Thumbi, S.M. Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis |
| title | Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis |
| title_full | Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis |
| title_fullStr | Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis |
| title_full_unstemmed | Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis |
| title_short | Integrated surveillance for human and animal brucellosis in Kenya: A predictive analysis |
| title_sort | integrated surveillance for human and animal brucellosis in kenya a predictive analysis |
| topic | animal health brucellosis disease control health zoonoses |
| url | https://hdl.handle.net/10568/178734 |
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