Estimating burden of foodborne diseases where public health impact is higher and data scarcer: a study in four African countries
Bibliographic record
Abstract
Abstract Low and middle-income countries, in particular from Africa, bear the highest burden of foodborne disease (FBD). However, because research and disease surveillance data from Africa are limited, previous burden estimates are subject to uncertainty. The main challenge to estimating burden of FBD in Africa is lack of data, where factors ranging from lack of capacity to lack of political commitment, and a focus on priority diseases, limit existing surveillance systems. To address this, we are working with Ethiopia, Mozambique, Nigeria, and Tanzania, to estimate the burden of, and strengthen surveillance systems for, FBD in Africa. We are conducting a population survey (to estimate incidence and distribution of diarrhea in the community), a systematic literature review (to estimate proportions of diarrheal disease caused by different agents), and an active review of available FBD reports (to estimate the extent of under-reporting in existing surveillance). Together, these findings will provide more accurate estimates of the burden of FBD for African countries. Lessons from this large-scale project can be extrapolated to other countries and regions where the burden is high but data are scarce. We highlight applying leadership attributes, including delegation of duties, setting milestones, regular meetings, transparency, and risk mitigation plans. The leading role of experts in this project helps to reduce hurdles. We have also adapted existing data collection tools for use across our diverse African study populations. We are engaging stakeholders who will use our research outputs, by involving them at all stages of the project. This integrated Knowledge Translation approach is translatable to other settings. These studies are part of FOCAL (Foodborne Disease Epidemiology, Surveillance, and Control in African LMIC), a multi-partner, multi-study project co-funded by the Bill and Melinda Gates Foundation and the United Kingdom's Department for International Development.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".