Surveillance and control of neglected zoonotic diseases: methodological approaches to studying Rift Valley Fever, Crimean-Congo Haemorrhagic Fever and Brucellosis at the human-livestock-wildlife interface across diverse agricultural systems in Uganda
Bibliographic record
Abstract
Abstract Background Zoonoses are of public health importance, with most major emerging diseases originating in animal populations. Rift Valley Fever (RVF), Crimean-Congo Haemorrhagic Fever (CCHF) and Brucellosis are circulating in Uganda, causing frequent outbreaks, but gaps exist in the understanding of transmission dynamics, community perspectives and effective mitigation strategies of these diseases. With increasing human-livestock-wildlife interaction in Uganda’s biodiverse cattle corridor, this study protocol outlines an integrated One Health model to determine the burden of RVF, CCHF and Brucellosis, identify key vectors and reservoirs and assesses the impact of social and policy factors on disease distribution. Methods A series of mixed-methods cross-sectional and longitudinal surveys across six conservation areas experiencing high human-livestock-wildlife interaction spanning Uganda’s Cattle Corridor: Queen Elizabeth National Park, Bwindi-Mgahinga Impenetrable Forest, Lake Mburo-Nakivaale, Murchison Falls, Kidepo Valley and Pian Upe Game Reserve. In selected villages, household surveys comprise questionnaires, focus-group discussions and in-depth interviews to determine drivers of disease risk with blood-sampling of human population. Questionnaires provide details on livestock practices, and blood sampling is conducted on cattle, sheep, pigs and goats. Targeted sampling of vectors in these localities, including mosquitoes, ticks and small mammals, using environmental traps and on-host collection. Specimens taken from nearby large wildlife include blood sampling and nasal swabs. Serological testing using indirect ELISA and molecular testing using real-time PCR was conducted to determine disease status of RVF, CCHF and Brucellosis across humans, livestock and wildlife with eco-epi modelling and qualitative analyses used to inform risks and drivers of disease. Results Baseline survey data and blood specimens were obtained from 2894 humans residing in 1602 households across 96 villages in 6 conservation areas of Uganda. A further 379 community members participated in focus group discussions and key informant interviews with 978 reformed and active poachers across 4 conservation areas. 3692 livestock were sampled, including 1925 cattle, 1409 goats, 282 sheep and 76 pigs from 358 herds. Vector data were collected for 18236 ticks, 53480 mosquitoes and 612 rodents. 241 large wildlife were sampled, including buffalo, kobs, zebras, waterbucks, topi and hartebeest. 127 Community One Health Volunteers (COHVs) were enlisted to monitor and detect outbreaks in the study sites. Conclusions This paper outlines a comprehensive One Health approach to studying neglected zoonotic diseases, integrating molecular epidemiology, social sciences and community participatory approaches involving humans, livestock, vectors and wildlife across 6 conservation areas in Uganda. It will inform interventions to enhance the surveillance and control of RVF, CCHF and Brucellosis, including strengthening outbreak preparedness and response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.097 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".