AIMC Topic: Mosquito Control

Clear Filters Showing 1 to 10 of 15 articles

EVALUATING VECTECH IDX™: AI-DRIVEN IDENTIFICATION FOR ENHANCED VECTOR MANAGEMENT.

Journal of the American Mosquito Control Association
Assessing and advancing cutting-edge technologies that are designed to optimize mosquito surveillance strategies is crucial given the complex challenges presented by our rapidly changing environments. Vectech's Identification-X (IDX) machine offers a...

Leveraging machine learning to predict mosquito bed net utilization among women of reproductive age in sub-Saharan Africa.

Malaria journal
BACKGROUND: Malaria remains a major public health challenge, particularly in sub-Saharan Africa, where women of reproductive age are especially vulnerable during pregnancy and childbirth. To identify key predictors and improve predictive accuracy, ma...

The role of artificial intelligence for dengue prevention, control, and management: A technical narrative review.

Acta tropica
Dengue fever remains a significant global health threat, particularly in tropical and subtropical regions, where rapid urbanization and climate variability exacerbate its spread. Traditional surveillance and control systems often struggle with delaye...

Understanding the determinants of treated bed net use in Ethiopia: A machine learning classification approach using PMA Ethiopia 2023 survey data.

PloS one
INTRODUCTION: Malaria remains a significant public health challenge in Ethiopia, with over 7.3 million cases and 1,157 deaths reported between January 1 and October 20, 2024. Despite extensive distribution campaigns, 35% of insecticide-treated nets (...

Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap.

Parasites & vectors
BACKGROUND: The age distribution of a mosquito population is a major determinant of its vectorial capacity. To contribute to disease transmission, a competent mosquito vector, carrying a pathogen, must live longer than the extrinsic incubation period...

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes.

Acta tropica
Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existin...

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus.

Acta tropica
Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts i...

Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control.

PloS one
Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in ...

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso.

Parasites & vectors
BACKGROUND: Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the...

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time.

Scientific reports
Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using har...