Potential Mammalian Vector-Borne Diseases in Live and Wet Markets in Indonesia and Myanmar
Bibliographic record
Abstract
Vector-borne diseases spread from wild animals and their associated ectoparasites to humans and domesticated animals. Wildlife markets are recognized as important areas where this transfer can take place. We assessed the potential for spreading vector-borne diseases in two live and wet markets in Myanmar (Mong La, on the Myanmar-China border) and Indonesia (Sukahaji in Bandung on the island of Java) by making an inventory of all live and freshly killed wild mammals for sale. For eight mammal families, we quantified the number of animals on offer, and we used a heatmap cluster analysis to map vector-borne diseases that these families may carry. In Myanmar, we observed large numbers of wild pigs and deer (potentially carrying West Nile and various encephalitis viruses) whereas in Indonesia we observed Old World fruit bats (potentially carrying Chikungunya and encephalitis viruses) and squirrels (potentially carrying West Nile and encephalitis viruses). The trade in Indonesia was dominated by live mammals offered for sale as pets, and only Old World fruit bats and squirrels traded for traditional Asian medicine were killed in the markets. The trade in Myanmar was more geared towards wild meat (e.g., wild pigs, deer, primates) and traditional Asian medicine (squirrels). The combined risks of vector-borne diseases spreading from traded animals to human health highlight the need for an integrated approach protecting public health, economic interests and biodiversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".