The global burden of neglected zoonotic diseases: Current state of evidence
Bibliographic record
Abstract
The majority of emerging infectious diseases are zoonoses, most of which are classified as "neglected". By affecting both humans and animals, zoonoses pose a dual burden. The disability-adjusted life year (DALY) metric quantifies human health burden since it combines mortality and morbidity. This review aims to describe and analyze the current state of evidence on neglected zoonotic diseases (NZDs) burden and start a discussion on the current understanding of the global burden of NZDs. We identified 26 priority NZDs through consulting three international repositories for national prioritization exercises. A systematic review of global and national burden of disease (BoD) studies was conducted using pre-selected databases. Data on diseases, location and DALYs were extracted for each eligible study. A total of 1887 records were screened, resulting in 74 eligible studies. The highest number of BoD was found for non-typhoidal salmonellosis (23), whereas no estimates were found for West Nile, Marburg and Lassa fever. Geographically, the highest number of studies was performed in the Netherlands (11), China (5) and Iran (4). The number of BoD retrieved mismatched the perceived importance in national prioritization exercises. For example, anthrax was considered a priority NZD in 65 countries; however, only one national study estimating BoD was retrieved. By summing the available global estimates, the selected NZDs caused at least 21 million DALYs per year, a similar order of magnitude to (but less than) the burden due to foodborne disease (included in the Foodborne Disease Burden Epidemiology Reference Group). The global burden of disease landscape of NZDs remains scattered. There are several priority NZDs for which no burden estimates exist, and the number of BoD studies does not reflect national disease priorities. To have complete and consistent estimates of the global burden of NZDs, these diseases should be integrated in larger global burden of disease initiatives.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".