The Mosquito Alert dataset includes occurrence records of adult mosquitoes. The records were collected through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Each record presented in the database is linked to a photograph submitted by a citizen scientist and validated by entomological experts to determine if it provides evidence of the presence of any of five targeted mosquito vectors of top concern in Europe (i.e. Aedes albopictus, Aedes aegypti, Aedes japonicus, Aedes koreicus, Culex pipiens). The temporal coverage of the database is from 2014 through 2022 and the spatial coverage is worldwide. Most of the records from 2014 to 2020 are from Spain, reflecting the fact that the project was funded by Spanish national and regional funding agencies. Since autumn 2020 the data has expanded to include substantial records from other countries in Europe, particularly the Netherlands, Italy, and Hungary, thanks to a human volunteering network of entomologists coordinated by the AIM-COST Action and to technological developments through the VEO project to increase scalability. Among many possible applications, Mosquito Alert dataset facilitates the development of citizen-based early warning systems for mosquito-borne disease risk. This dataset can be further re-used for modelling vector exposure risk or training machine-learning detection and classification routines on the linked images, to help experts in data validation and build up automated alert systems.

Counts of mosquitoes by year, week, and species collected by the Chicago Department of Public Health (CDPH) Environmental Health program. CDPH maintains an environmental surveillance program primarily for monitoring West Nile Virus (WNV) but identifies and sorts all mosquitoes collected. Currently this dataset only contains data for Aedes albopictus but will expand to include additional species in the future.

A large-scale multi-species dataset of acoustic recordings

Dataset accompanying code and paper: HumBugDB: a large-scale acoustic mosquito dataset.

A large-scale multi-species dataset containing recordings of mosquitoes collected from multiple locations globally, as well as via different collection methods. In total, we present 71,286 seconds (20 hours) of labelled mosquito data with 53,227 seconds (15 hours) of corresponding background noise, recorded at the sites of 8 experiments. Of these, 64,843 seconds contain species metadata, consisting of 36 species (or species complexes).

This repository contains:

• Multi-part zip of audio files to be extracted into the same folder
• Metadata in csv format: neurips_2021_zenodo_0_0_1.csv

This data is supplemented by a GitHub repository, https://github.com/HumBug-Mosquito/HumBugDB, which aids as follows:

• The multi-part zip is intended to be extracted into the folder: /data/audio/ in the repository.
• Latest metadata is hosted on GitHub to allow the modification of additional metadata as it becomes available in the database or bug-fixing.
• Documentation for code use, and a complete Datasheet for Datasets also available on GitHub.
• Example code for data splitting, feature extraction, model training, and evaluation in the top-level notebook main.ipynb.
• Bayesian Convolutional Neural Network models, in both Keras and PyTorch, trained on this data available at GitHub release v1.0

These data represent mosquito trap site results in the District of Columbia from 2016 to 2018. Trap locations are considered approximate address and/or the “nearest” street address or block to the stated coordinates in the data. Visit Fight the Bite: Protecting the District of Columbia from Mosquitoes- a collection of the 2016-2018 Arbovirus Surveillance Program conducted annually by DC Health, Health Regulation & Licensing Admin., Animal Services Div.Mosquitoes have the potential to spread harmful diseases. During the annual mosquito season in Washington DC, usually from April – October, DC Health deploys surveillance and mitigation methods to control the mosquito population in the District. DC Health (also known as the D.C. Department of Health or formerly DOH) has been trapping and testing mosquitoes for West Nile virus (WNV) for well over a decade. Starting in 2016, and in response to the Zika outbreak in Latin America and the Caribbean, DC Health substantially increased mosquito monitoring activities across the city. There were a total of 28 sites and 36 traps across the 8 wards. Data was submitted to the Centers for Disease Control MoquitoNet Portal.Note: the 2017 analysis does not include data for October. This is because October of 2017 would have skewed the results far too much based on a few variables that occurred. For example, the number of traps which had failed by the end of the season.Mosquito species in Washington, D.C.:Culex Pipiens, Salinarius and Culex Restuan: spread West Nile VirusAedes aegypti : according to the Centers for Disease Control (CDC), health experts have determined this species to be the most competent vector, capable of transmitting Zika to the human population. To date, none of the Aedes aegypti trapped in Washington, D.C. have been found to carry the Zika virus.Aedes albopictus: capable of spreading Zika to people. However, health experts are still learning whether it is likely to do so as it appears at this time, it is not as competent a vector for transmitting Zika as is the Aedes aegypti. Just because a mosquito can carry the virus does not mean that it will cause disease. So far, none of the Aedes albopictus trapped in Washington, D.C. have been found to carry the Zika virus.Aedes japonicus: normally found in South Florida, is present in D.C. in small numbers. Presently there is no indication that they are competent vectors for spreading Zika to the human population.

MODIRISK aims at studying biodiversity of mosquitoes and monitoring/predicting its changes, and hence actively prepares to address issues on the impact of biodiversity change with particular reference to invasive species and the risk to introduce new pathogens. This is essential in the perspective of the ongoing global changes creating suitable conditions for the spread of invasive species and the (re)emergence of vector-borne diseases in Europe. The main strengths of the project in the context of sustainable development are the link between biodiversity and health-environment, and its contribution to the development of tools to better describe the spatial distribution of mosquito biodiversity. MODIRISK addresses key topics of the global initiative Diversitas, which was one of the main drivers of the 'Research programme Science for a Sustainable Development' (SSD). This dataset contains the monitoring data.

The project was coordinated by the Institute of Tropical Medicine (http://www.itg.be/E) in Antwerp.

iloncka/mosquito-species-classification-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Malaria and Yellow fever. Image based mosquito species classification can be helpful to implement strategies to prevent the spread of mosquito borne disease. Automated mosquito species classification can aid in laborious and time consuming task of entomologists besides enhancing accuracy.

The Aedes Mosquitoes Dataset contains 4,120 high-resolution JPEG images of Aedes aegypti and Aedes albopictus, two species globally recognized as vectors for dengue, Zika, chikungunya, and yellow fever. This dataset is designed for developing deep learning models in computer vision and public health research.

Mosquito

## Overview

Mosquito is a dataset for object detection tasks - it contains Mosquito annotations for 5,543 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Files are labeled using the filenames. The file names are shown as: genus_species_sex_strain_imagenumber.jpg

As part of a larger study looking at the efficacy of the biopesticide VectoMax FG for control of larval Culex quinquefasciatus, USGS and DOFAW personnel monitored adult mosquitoes (Culex quinquefasciatus and Aedes japonicus) along the Kawaikoi Stream during late summer, September through November 2016 and 2017. Ten trap sites were selected across a 1-kilometer grid centered on the intersection of the Alakai Swamp Trail and Kawaikoi Stream, Alakai Wilderness Preserve, Kauai. Traps were located at least 200 meters apart at accessible sites along the stream, valley floor, and adjacent plateau. Both Biogents Sentinel Traps (BGS) and Centers for Disease Control and Prevention (CDC) Gravid Traps (GRV) were operated nightly at each site from 1600 to 0700 hr the following morning. Depending on the weather (heavy rains and high water) and trap reliability (battery and CO2 delivery failures) the number of traps operated per night varied considerably. The data was used to compare the weekly relative abundance of mosquitoes (mosquitoes/trap-night) across the trapping season and following VectoMax FG application.

Detailed data on the past and present distribution of mosquito vectors.

Mosquito-borne avian malaria is a key limiting factor on Hawaiian forest bird populations. Preservation of endemic forest birds and restoration of Hawaiian forest bird communities will rely on mosquito control. While landscape level control is being developed managers need short term and reliable tools for monitoring and controlling mosquito populations to protect remaining breeding bird populations. As part of a larger study on the efficacy of the biopesticide VectoMax FG for control of larval Culex quinquefasciatus and adult mosquito traps for monitoring, USGS personnel evaluated host-seeking trap configurations and gravid trap lures for capturing adult mosquitoes (Culex quinquefasciatus and Aedes japonicus) in native forest habitat from August to November 2017. Four trap sites were selected in a forest tract in Volcano Village. Traps were arranged in a 100-meter square and trap types and lures were rotated through each site during each week of the study in a latin square design. Both host-seeking traps (Biogents Sentinel Traps and CDC miniature light traps) and CDC Gravid Traps were operated nightly at each site from 1600 to 0700 hr the following morning. Collected mosquitoes were maintained on a 3% sucrose solution and later dissected for malarial diagnostics. Midguts and salivary glands were examined under compound microscopy (450X) for evidence of infection. Oocyst presence and intensity and sporozoite presence and relative intensity were recorded. The data provides for a comparison of trap types and lures for deriving malaria prevalence data and a useful measure of transmission risk at this time and place.

This collection contains vials of identified mosquitoes stored in bulk (not pathogen tested) from CO2 trapping at terrestrial sites (NEON sample class: mos_archivepooling_in.archiveVialIDList). When adult mosquitoes are active, sampling occurs (via CDC light traps) every two weeks at core sites and every four weeks at gradient sites. A sampling bout consists of one trapping night and the following day for up to ten plots per site. Following collection, samples are sent to a professional taxonomist where a subsample of each catch generated from each trap is identified to species and sex. Starting with samples collected in field year 2025, bloodfed mosquitos are archived seperately, but samples collected prior to 2025 may contain bloodfed mosquitos. Vials containing identified mosquitoes are archived in 2, 5, 10, or 15 mL cryovials in liquid nitrogen, or at -80 for samples collected before 2020. See related links below for protocols and NEON related data products.

This statistic shows the capacity of one of the main mosquitoes (Aedes aegypti) to transmit dengue fever worldwide from 1950 to 2014. In the year 1951, the capacity of this mosquito to transmit dengue fever was 1.35 percent, whereas this capacity rose to 8.68 percent in 2014. The capacity of mosquitoes to transmit dengue fever in countries where the disease is epidemic has increased due to changing climatic conditions in these countries.

Dataset

This dataset was created by saidineshpola

Contents

We collected images from 797 female mosquito specimens with 198 - 200 specimens of four different species: Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus (Ae. japonicus) (Table 1). All specimens were reared under standardized conditions in the arthropod rearing facility at the Bernhard Nocht Institute for Tropical Medicine, Hamburg. Each specimen was photographed using three different devices: a smartphone (iPhone SE 3rd Generation, Apple Inc., Cupertino, USA), a macro-lens (Apexel-25MXH, Apexel, Shenzhen, China) connected to the same smartphone, and a stereomicroscope (Olympus SZ61, Olympus, Tokyo, Japan) with an attached camera (Olympus DP23, Olympus, Tokyo, Japan). In the following text, we will refer to the smartphone as a “phone”, the smartphone with a macro lens attachment as “macro-lens” or “macro”, and the stereomicroscope as “microscope” or “micro”. For the “body” dataset, the complete mosquitoes were photographed with all three devices in the same orie...

List of locations and test results for pools of mosquitoes tested through the Chicago Department of Public Health Environmental Health program. The Chicago Department of Public Health maintains an environmental surveillance program for West Nile Virus (WNV). This program includes the collection of mosquitoes from traps located throughout the city; the identification and sorting of mosquitoes collected from these traps; and the testing of specific species of mosquitoes for WNV.

This dataset tracks the long-term mosquito monitoring program and the mosquito spray study. Public dashboard is at: https://stories.opengov.com/sugarlandtx/published/0MQDauinr

Automatic Detection of Mosquito Breeding Grounds using YOLOv7 and Transformer Prediction Head. This project implements a deep learning algorithm for the automatic detection of mosquito breeding grounds in images and videos. The algorithm is based on a pre-trained YOLOv7 model with a Transformer Prediction Head, fine-tuned on a custom dataset of mosquito breeding sites. The dataset was created from a subset of the SMT Lab (UFRJ) dataset and includes 5,094 annotated images. The algorithm can detect mosquito breeding sites in real-world scenarios with various lighting and weather conditions, making it a useful tool for public health officials and researchers. This project was developed as part of a challenge for a conference on the detection of mosquito breeding grounds.