Maxent modeling for predicting the potential distribution of global <i>talaromycosis</i>
Bibliographic record
Abstract
Abstract Talaromycosis , an invasive mycosis caused by Talaromyces marneffei (Tm), has rapidly increased in recent years, becoming an emerging pathogenic fungal disease. However, The driving factors and potential distribution of global talaromycosis is still unclear. Here, we developed maxent ecology model using environmental variables, Rhizomys distribution and HIV/AIDS epidemic to forecast ecological niche of talaromycosis worldwhile, as well as Identify the drivering factors. The constructed model had excellent performance with the area under the curve (AUC) of the receiver operating curve (ROC) of 0.997 in training data and 0.991 in testing data. Our model revealed that Rhizomys distribution, mean temperature of warmest quarter, precipitation of wettest month, HIV/AIDS epidemic and mean temperature of driest quarter were the top 5 important variables affecting talaromycosis distribution. In addition to traditional talaromycosis epidemic areas (South of the Yangtze River in China, Southeast Asian and North and Northeast India), our model also identified other potential epidemic regions, inculding parts of the North of the Yangtze River, Central America, West Coast of Africa, East Coast of South America, the Korean Peninsula and Japan. Our findings has redefined global talaromycosis , discovered hidden high-risk areas and prorvided insights about driving factors of talaromycosis distribution, which will help inform surveillance strategies and improve the effectiveness of public health interventions against Tm infections. Author Summary Our study aims to explore the spatial ecology of talaromycosis worldwhile. The diseases burden of Talaromycosis , a neglected zoonotic disease, is continuously rising in recent years because of the sheer size of susceptible population in the setting of increased globalization, rising HIV prevalence, and emerging iatrogenic immunodeficiency conditions. Here, we used historic reported talaromycosis cases from 1964 to 2017, combined with environmental factors, Rhizomys distribution and HIV/AIDS epidemic to build an maxent ecology model to define the ecological niche of talaromycosis , then predicting the potential distribution of the disease. The ecological niche of talaromycosis is characterized by a concentrated distribution, which can be cognitively divided into two regions: traditional talaromycosis epidemic areas (South of the Yangtze River in China, Southeast Asian and North and Northeast India), while other potential epidemic regions were predicted in parts of the North of the Yangtze River, Central America, West Coast of Africa, East Coast of South America, the Korean Peninsula and Japan. Our model also identified 5 driving factors affecting talaromycosis distribution. These findings will help demonstrate the global distribution of talaromycosis, discover hidden high-risk areas, and improve the effectiveness of public health interventions against Tm infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".