Modeling the impact of climate change for the potential distribution of the main vector and reservoirs of zoonotic cutaneous leishmaniasis due to leishmania major in Morocco
Bibliographic record
Abstract
Climate change is reshaping the epidemiology of vector-borne diseases, with zoonotic cutaneous leishmaniasis (ZCL) caused by Leishmania major emerging as a growing public health concern in Morocco. This study employs ecological niche modeling (ENM) to assess the current distribution and project future impacts of climate change on L. major , its primary vector ( Phlebotomus papatasi ), and reservoir host ( Meriones shawi ) under four Representative Concentration Pathway (RCP) scenarios (2.6, 4.5, 6.0 and 8.5). Under present climate conditions, our models reveal distinct distribution patterns: L. major is concentrated in southeastern Morocco, P. papatasi is widespread across central regions, and M. shawi occupies nearly nationwide distribution except Western Sahara. Projections indicate L. major will extend its range into eastern, High Atlas, and Rif regions (1.5–1.6% habitat gain), while P. papatasi and M. shawi will expand across central and southern Morocco (3.5–5.9% gain), with minimal habitat loss (<0.6%). These findings demonstrate a possible climate-driven shift in ZCL transmission geography, with current endemic areas expanding and new risk zones emerging in previously unaffected regions. The projections underscore the urgent need for integrated surveillance and climate-adaptive control strategies to mitigate outbreaks in vulnerable regions. By linking observed distributions to future environmental shifts, this work provides a framework for proactive public health interventions in Morocco and similar endemic areas facing climate change impacts.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".