Multiscale environmental determinants of Leishmania vectors in the urban-rural context
Bibliographic record
Abstract
BACKGROUND: In South America, cutaneous leishmaniasis (CL) and visceral leishmaniasis (VL) are emerging diseases, expanding in the border area of Argentina, Brazil and Paraguay. Outbreaks of CL were reported since the 1990s, with Nyssomyia whitmani as the main vector in this region. Regarding VL, urban reports started in 2010 with Lutzomyia longipalpis as the main vector. The aim of this study was to evaluate environmental determinants related to the main vectors of leishmaniasis, to contribute to the prevention and control response to the emergence of VL and CL in the Argentina-Brazil-Paraguay border region. METHODS: The cross-sectional survey includes two cities and two close rural areas in the Argentinean Northeast Region, between November 2014 and January 2015, with a total of 95 sampling sites. REDILA-BL traps were set for three consecutive nights, and a total of 68 meso- and microscale environmental and landscape characteristics were surveyed. The association between vector abundance with different variables was evaluated using a generalized linear model with zero-inflated negative binomial distribution. We analyzed females for detection of Leishmania DNA. RESULTS: The analysis for Lu. longipalpis indicates an excess of absences when the mean NDWI around the sites were higher. The abundance of Lu. longipalpis at mesoscale level was higher when more urban services were present, and when blood sources such as chickens or dogs at the microscale level were present. For Ny. whitmani, no variable was found to be associated with the absences, while its abundance increased in association with the following variables: percentage of tree cover, presence of garbage collection service, hosted people and, at microscale, the presence of poultry. Leshmania infantum DNA was detected in 2/49 (4%) Lu. longipalpis. CONCLUSIONS: The abundance of both species is influenced by variables at different scales, their influence probably has a hierarchy and they are acting on different aspects of the biology of these vectors. The urban spatial segregation of Lu. longipalpis and the peri-urban and rural segregation of N. whitmani increase the risk of VL and CL. The selection of the better variables for each scale will allow the design of appropriate control strategies depending on species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".