The mosquito species Aedes aegypti and Aedes albopictus are the primary vectors of the arboviruses dengue, Zika, and chikungunya. Expansion of these vectors into previously non-endemic regions due to climate and environmental changes has accelerated global burden from arboviral diseases. To combat this, predictive models accurately mapping Aedes habitats are essential for epidemiological modelling, effective vector control, and disease prevention. We introduce the Climademic Suitability Model, a machine learning model that delivers monthly global predictions of Aedes habitat suitability at 0.25degrees spatial resolution between 1975-2024. The model leverages integrated climate, land use, human population, and mosquito surveillance data to provide an explainable view of the factors governing habitat dynamics. SHAP-based explainability analysis identified temperature and dew point temperature as dominant features driving habitat suitability. Long-term analysis reveals a complex global redistribution of expanding and contracting vector habitats. Suitable areas for both species now encompass regions home to over 5 billion people, coinciding with the world’s most pronounced population growth and surpassing projections previously placing this threshold at 2050. The Climademic Suitability Model serves as a framework for near-real-time vector surveillance, climate scenario projection, and integration into transmission models to advance epidemic preparedness in an era of accelerating environmental change.
SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images
arXiv:2604.15777v1 Announce Type: cross Abstract: Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is