WingShape-Warn: A Morphology-Driven Early-Warning Pipeline for Mosquito-Borne Disease Surveillance

Environmental stressors—including climate change, urbanisation, and pollution—are increasingly altering the wing morphology of mosquito vectors. These morphological shifts, particularly in wing geometry, can serve as early-warning indicators for mosquito abundance and arboviral disease outbreaks. However, quantitative wing-shape analysis remains underutilised in operational mosquito surveillance and public health early-warning systems. This Technology and Code article presents WingShape-Warn, a reproducible, open-source pipeline that integrates geometric morphometrics, deep learning-based image segmentation, association-rule mining, and Neo4j knowledge-graph construction to detect, quantify, and visualise morphology-driven mosquito-borne disease risk. The protocol comprises seven steps: (1) stratified collection of mosquito wing images along environmental gradients; (2) image preprocessing and deep learning-based landmark/semilandmark extraction; (3) generalised Procrustes analysis (GPA), principal component analysis (PCA), and Apriori association-rule mining on wing-shape variables; (4) Neo4j knowledge-graph construction linking wing-shape traits, environmental variables, and disease outcomes; (5) design of a morphology-based early-warning rule base; (6) automated report generation with a knowledge-update trigger; and (7) validation via retrospective forecasting and receiver operating characteristic (ROC) analysis. WingShape-Warn has been validated on 1200 wing images of Aedes albopictus and Anopheles spp. (2020–2024, five climate zones), demonstrating that wing-shape PC1 explains 42.3% of the variance and predicts mosquito abundance with an AUC of 0.89. WingShape-Warn has been measured and validated on 1200 wing images of Aedes albopictus and Anopheles spp. (2020–2024, five climate zones)—the only corpus measured in this study—demonstrating that wing-shape PC1 explains 42.3% of the variance and predicts mosquito abundance with an AUC of 0.89. Two reporting conventions are adopted throughout: results obtained on this mosquito corpus are reported as measured results, whereas all cross-taxa material is reported as illustrative, literature-mapped examples and is labelled [Illustrative] wherever it appears. An ablation on the held-out chronological test set shows that adding wing-shape PCs to environmental variables alone raises the AUC from 0.74 to 0.89 (ΔAUC = +0.15, DeLong p < 0.001; NRI = 0.21, IDI = 0.14), while a permuted-shape control returns the model to baseline (AUC 0.75, p = 0.62). The U-Net landmark detector reaches a mean radial error of 1.34 ± 0.41 px against manual digitisation under five-fold cross-validation, i.e., below the pre-registered 2 px tolerance and within human inter-operator repeatability (1.12 ± 0.33 px). While the measured validation is currently limited to mosquito wings, the modular “analyze-detect-warn” architecture can be reused or adapted by researchers for other arthropod vectors (e.g., ticks, sandflies) or additional environmental stressors; such extensions are presented here as proof-of-concept demonstrations and require independent validation before operational use.