Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep Learning

2022/08/01 by Christopher Sun, Sun, Christopher, Jay Nimbalkar +3
Medicine · Social Sciences · #Dengue and Mosquito Control Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Malaria Research and Control #Mosquito-borne diseases and control

paper · pdf · doi:10.48550/arxiv.2208.01436

openalex publication_date 2022/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and larvae observations, a neural network is trained to predict larvae counts from ecological inputs. Time series forecasting is conducted on these variables and climate projections are passed into the initial deep learning model to generate location-specific larvae abundance predictions. The results support the notion of regional ecosystem-driven changes in mosquito spread, with high-elevation regions in particular experiencing an increase in susceptibility to mosquito infestation.

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