AIMC Topic: Mosquito Vectors

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Microclimates, land cover, and socioeconomic vulnerability shape Anopheles hotspots in Maryland, USA.

Infectious diseases of poverty
BACKGROUND: Anopheles mosquitoes pose notable public health concerns as competent vectors of malaria and other diseases. Although malaria is no longer endemic in the United States, recent locally acquired cases in states including Maryland highlight ...

SURVEILLANCE OF WEST NILE VIRUS IN MARYLAND: INTEGRATING SMART VECTOR IDENTIFICATION WITH ENVIRONMENTAL AND EPIDEMIOLOGICAL INSIGHTS.

Journal of the American Mosquito Control Association
This study presents an integrated, operational mosquito surveillance effort conducted in Anne Arundel County, Maryland, during the 2023 and 2024 seasons, revealing substantial variation in Culex pipiens s.l. abundance and West Nile virus (WNV) infect...

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...

Towards scalable age-grading of Aedes albopictus mosquito using mid-infrared spectroscopy and machine learning.

Scientific reports
The age structure and dynamics of mosquito populations are crucial for understanding their ability to spread diseases and assessing the effectiveness of anti-mosquito control measures. However, available methods to age-grade mosquito populations are ...

Comparing machine learning, deep learning, and reinforcement learning performance in Culex pipiens predictive modeling.

PloS one
Several machine learning (ML) and deep learning (DL) methods have been used to predict the presence of species in classification problems. Another set of methods, called reinforcement learning (RL), has been used in training agents to perform various...

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

Scientific reports
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 coar...

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...

Assessment of the transmission of live-attenuated chikungunya virus vaccine VLA1553 by Aedes albopictus mosquitoes.

Parasites & vectors
BACKGROUND: Chikungunya virus (CHIKV) is a mosquito-transmitted, arthritogenic alphavirus that causes sporadic outbreaks of often debilitating rheumatic disease. The recently approved CHIKV vaccine, IXCHIQ, is based on a live-attenuated CHIKV strain ...

Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.

Parasites & vectors
BACKGROUND: Mosquito-borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region of Spain, changing ...

Discrimination of inherent characteristics of susceptible and resistant strains of Anopheles gambiae by explainable artificial intelligence analysis of flight trajectories.

Scientific reports
Understanding mosquito behaviours is vital for the development of insecticide-treated nets (ITNs), which have been successfully deployed in sub-Saharan Africa to reduce disease transmission, particularly malaria. However, rising insecticide resistanc...