Effect of temperature, rainfall and relative density of rodent reservoir hosts on zoonotic cutaneous leishmaniasis incidence in Central Tunisia
Bibliographic record
Abstract
Objective: To study the effect of climate variability and rodent density on the incidence of zoonotic cutaneous leishmaniasis (ZCL) in humans.Methods: We collected monthly ZCL human cases in primary health care facilities, in schools and in the community.We collected monthly climate parameters such as temperature, humidity, rainfall, wind direction and wind speed, and rodent density.We investigated the relationship between ZCL incidence and climate and environmental variables by univariate and different multivariable analysis (multiple linear regression, negative binomial regression and autoregressive integrated moving average). Results:The ZCL number peaked in October and November.In univariate analysis, positive associations were found for the maximum, mean and minimum temperatures lagged for three and six months, with higher correlation coefficient for the mean temperature lagged for six months (r = 0.837, P < 0.01).All multivariate analyses showed positive association between monthly ZCL incidence and the six months moving average temperature with higher correlation coefficients and very small significant level, whereas negative association was observed for the cumulative rainfall of the last year.Conclusions: This work showed a significant association between ZCL incidence and climate variables suggesting that ecological early warning system could be applied for ZCL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".