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Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables

Abstract Effective prediction of Aedes mosquito abundance and dengue risk indicators such as the Aedes Index (AI) and Dengue Positive Trap Index (DPTI) is essential for early intervention and targeted vector control. However, current models often rely on coarse regional data and fail to account for...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Nazri Che Dom, Nur Athen Mohd Hardy Abdullah, Rahmat Dapari, Siti Aekbal Salleh
Format: Artigo
Sprache:Inglês
Veröffentlicht: Nature Portfolio 2025-10-01
Schriftenreihe:Scientific Reports
Schlagworte:
Dengue
Aedes mosquito
Predictive modeling
Machine learning
Microclimate
Risk mapping
Online-Zugang:https://doi.org/10.1038/s41598-025-17191-y
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