Composite function and Biomod2 for evaluating the influence of climate change on the distribution of Aedes aegypti and Aedes albopictus in China
Bibliographic record
Abstract
Vector-borne diseases transmitted by Aedes, including dengue fever, Chikungunya fever, Zika virus, and yellow fever, represent major global public health threats. This study utilized the Biomod2 modeling framework, incorporating 19 bioclimatic variables, to simulate the current and future geographical distributions of Aedes aegypti and Aedes albopictus in China under climate change scenarios (SSP2–4.5 and SSP5–8.5). The results indicated that under future climate scenarios, highly suitable regions for both Aedes would decrease in area, while moderately suitable regions would expand. The co-presence probability analysis revealed that highly suitable regions for both species would concentrate in southern and southeastern China, with notable areas in Yunnan, Guangxi, Guangdong, and Hainan. From current to 2090s, the centroid would shift to northeast under SSP2–4.5 and SSP5–8.5. For Ae. aegypti , the most important variables were isothermality (bio3, 44.05 %), precipitation of the wettest quarter (bio16, 27.87 %), and mean temperature of the coldest quarter (bio11, 22.4 %). For Ae. albopictus , the mean temperature of the coldest quarter (bio11, 54.12 %), annual precipitation (bio12, 22.76 %), and precipitation of the coldest quarter (bio19, 13.47 %) were most significant. These findings highlight the potential impacts of climate change on the distribution dynamics of dengue vectors and provide a basis for developing targeted surveillance and control strategies to mitigate future transmission risks.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".