Participatory epidemiology methods for foot and mouth disease surveillance.
Bibliographic record
Abstract
Participatory epidemiology (PE) is the application of participatory rural appraisal techniques to epidemiological studies and disease surveillance. The use of PE techniques in disease surveillance is termed participatory disease surveillance (PDS), a decision-oriented approach for the collection of epidemiological intelligence. In PDS, surveillance is defined as information for action. The techniques of PDS were first developed as part of the global eradication of rinderpest. Participatory surveillance made significant contributions to the program by uncovering some of the last foci of disease. Since that time, PDS has been adopted by several official veterinary services around the world as a form of targeted surveillance in national control programs. Surveillance applications have included avian influenza, classical swine fever, peste des petits ruminants, Rift valley fever and foot and mouth disease (FMD). The flexibility, timeliness and sensitivity of PE and PDS can enhance the effectiveness of surveillance programs in both developed and developing country contexts. The capacity building process to establish PE relies on consultation between national stakeholders to conceptualize national epidemiological objectives, formulate the components of the comprehensive epidemiological program and then to build a training program to develop personnel with the key skills to put the research and surveillance plan in action. The Participatory Epidemiology Network for Animal and Public Health (PENAPH) has been established to help meet the demand for enhancement of epidemiological services. It does this through support for capacity building in PE, helping to capture lessons on good practice and to carrying out research to refine approaches for solving epidemiological problems. PENAPH takes an ecohealth approach and is built on a core partnership of seven complimentary organizations. These are the World Organisation for Animal Health, the UN Food and Agriculture Organization, the Interafrican Bureau for Animal Resources of the African Union, the International Livestock Research Institute, Veterinaires sans Frontieres – Belgium, Veterinarians without Borders/Veterinaires sans Frontieres – Canada and the Royal Veterinary College. The network is currently seeking partners in the public health field to further strengthen activities on the animal-human interface. In regard to FMD, participatory epidemiology has been used in original research, targeted assessments, economic evaluations and national surveillance programs. Documented studies include Catley et al (2004) who used participatory techniques to explore the association between chronic heat intolerance and FMD. In Pakistan, FMD was included as a target disease for the national PDS system (Mariner et al., 2001). The distribution and risk factors associated with persistence of FMD in Erzurum Province of Turkey have been documented (Admassu, 2005). Lastly, a participatory impact assessment leading to a cost benefit analysis for FMD control in a traditional livestock keeping community was completed in Western Upper Nile, southern Sudan (Barasa et al, 2005). These studies indicate that FMD is a disease readily recognized by livestock owners and that cattle keeping communities are an important source of knowledge to inform disease control strategies and analysis and targeting of health policies.
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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 | 0.037 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".