Intense transmission of visceral leishmaniasis in a region of northeastern Brazil: a situation analysis after the discontinuance of a zoonosis control program
Bibliographic record
Abstract
In São Luís, Maranhão, northeastern Brazil, the notification of visceral leishmaniasis (VL) cases intensified in 1982, showing endemic and epidemic patterns. In this city, the Center for Zoonoses Control (CZC) was an organization in charge of the control and prevention of the disease. However, technical and political reasons have led to a significant decline in the periodicity of its activities. Therefore, in this study we evaluated the epidemiological scenario of human visceral leishmaniasis (HVL) and the prevalence of the disease in dogs after the cessation of the CZC activities, covering the period of 2007 to 2016. The seroprevalence of canine leishmaniasis was determined based on clinical and serological profiles. HVL cases were notified using data provided by the Municipal Health Department of São Luís. A seropositivity rate of 45.8% (p = 0.0001) was found among dogs, 54% (p = 0.374) of which were asymptomatic. As for human cases, there were 415 notifications, with an increase in the incidence of the zoonosis observed during the aforementioned period. Thus, it can be inferred that after the control and surveillance activities were curtailed, there was an increase in the number of seropositive animals in circulation, acting as reservoirs of infection for dogs and humans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".