Identification Of Mosquito Species Research Articles

Mosquito species identification accuracy of early deployed algorithms in IDX, a vector identification tool

Mosquito-borne diseases continue to pose a great threat to global public health systems due to increased insecticide resistance and climate change. Accurate vector identification is crucial for effective control, yet it presents significant challenges. IDX - an automated computer vision-based device capable of capturing mosquito images and outputting mosquito species ID has been deployed globally resulting in algorithms currently capable of identifying 53 mosquito species. In this study, we evaluate deployed performance of the IDX mosquito species identification algorithms using data from partners in the Southeastern United States (SE US) and Papua New Guinea (PNG) in 2023 and 2024 . This preliminary assessment indicates continued improvement of the IDX mosquito species identification algorithms over the study period for individual species as well as average regional accuracy with macro average recall improving from 55.3% [Confidence Interval (CI) 48.9, 61.7] to 80.2% [CI 77.3, 84.9] for SE US, and 84.1% [CI 75.1, 93.1] to 93.6% [CI 91.6, 95.6] for PNG using a CI of 90%. This study underscores the importance of algorithm refinement and dataset expansion covering more species and regions to enhance identification systems thereby reducing the workload for human experts, addressing taxonomic expertise gaps, and improving vector control efforts.

Enhancing mosquito classification through self-supervised learning

Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model’s overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies.

Barcoding of Italian mosquitoes (BITMO): generation and validation of DNA barcoding reference libraries for native and alien species of Culicidae

BackgroundMosquitoes (Culicidae), as disease vectors, represent a risk for human health worldwide. Repeated introductions of alien mosquito species and the spread of invasive species have been recorded in different countries. Traditionally, identification of mosquitoes relies on morphological observation. However, morphology-based identification is associated with a number of potential disadvantages, such as the high level of specialisation of the operator and its limited applicability to damaged samples. In these cases, species identification is achieved through molecular methods based on DNA amplification. Molecular-based taxonomy has also enabled the development of techniques for the study of environmental DNA (eDNA). Previous studies indicated the 16S mitochondrial ribosomal RNA (rRNA) gene as a promising target for this application; however, 16S rRNA sequences are available for only a limited number of mosquito species. In addition, although primers for the 16S rRNA gene were designed years ago, they are based on limited numbers of mosquito sequences. Thus, the aims of this study were to: (i) design pan-mosquito 16S rRNA gene primers; (ii) using these primers, generate a 16S rRNA gene mosquito reference library (with a focus on mosquitoes present in Italy); and (iii) compare the discriminatory power of the 16S rRNA gene with two widely used molecular markers, cytochrome c oxidase subunit 1 mitochondrial gene (COI) and internal transcribed spacer 2 (ITS2).MethodsA total of six mosquito genera (28 mosquito species) were included in this study: Aedes (n = 16 species), Anopheles (5 species), Coquillettidia (1 species), Culex (3 species), Culiseta (2 species) and Uranotaenia (1 species). DNA was extracted from the whole mosquito body, and more than one specimen for each species was included in the analysis. Sanger sequencing was used to generate DNA sequences that were then analysed through the Barcode of Life Data Systems (BOLD). Phylogenetic analyses were also performed.ResultsNovel 16S rDNA gene, COI and ITS2 sequences were generated. The 16S rRNA gene was shown to possess sufficient informativeness for the identification of mosquito species, with a discriminatory power equivalent to that of COI.ConclusionsThis study contributes to the generation of DNA barcode libraries, focussed on Italian mosquitoes, with a significant increase in the number of 16S rRNA gene sequences. We hope that these novel sequences will provide a resource for studies on the biodiversity, monitoring and metabarcoding of mosquitoes, including eDNA-based approaches.Graphical abstract

Robust mosquito species identification from diverse body and wing images using deep learning

Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of convolutional neural networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (95% CI: 84.2–91.0) compared to body-based classification 78.9% (95% CI: 77.7–80.0). Nevertheless, there are notable limitations of CNNs as they perform reliably across multiple devices only when trained specifically on those devices, resulting in an average decline of mean accuracy by 14%, even with extensive image augmentation. Additionally, we also estimate the required training data volume for effective classification, noting a reduced requirement for wing-based classification compared to body-based methods. Our study underscores the viability of both body and wing classification methods for mosquito species identification while emphasizing the need to address practical constraints in developing accessible classification systems.Graphical abstract

A Handheld Tool for the Rapid Morphological Identification of Mosquito Species (VectorCam) for Community-Based Malaria Vector Surveillance: Summative Usability Study.

Malaria impacts nearly 250 million individuals annually. Specifically, Uganda has one of the highest burdens, with 13 million cases and nearly 20,000 deaths. Controlling the spread of malaria relies on vector surveillance, a system where collected mosquitos are analyzed for vector species' density in rural areas to plan interventions accordingly. However, this relies on trained entomologists known as vector control officers (VCOs) who identify species via microscopy. The global shortage of entomologists and this time-intensive process cause significant reporting delays. VectorCam is a low-cost artificial intelligence-based tool that identifies a mosquito's species, sex, and abdomen status with a picture and sends these results electronically from surveillance sites to decision makers, thereby deskilling the process to village health teams (VHTs). This study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction. The VectorCam system has imaging hardware and a phone app designed to identify mosquito species. Two users are needed: (1) an imager to capture images of mosquitos using the app and (2) a loader to load and unload mosquitos from the hardware. Critical success tasks for both roles were identified, which VCOs used to train and certify VHTs. In the first testing phase (phase 1), a VCO and a VHT were paired to assume the role of an imager or a loader. Afterward, they swapped. In phase 2, two VHTs were paired, mimicking real use. The time taken to image each mosquito, critical errors, and System Usability Scale (SUS) scores were recorded for each participant. Overall, 14 male and 6 female VHT members aged 20 to 70 years were recruited, of which 12 (60%) participants had smartphone use experience. The average throughput values for phases 1 and 2 for the imager were 70 (SD 30.3) seconds and 56.1 (SD 22.9) seconds per mosquito, respectively, indicating a decrease in the length of time for imaging a tray of mosquitos. The loader's average throughput values for phases 1 and 2 were 50.0 and 55.7 seconds per mosquito, respectively, indicating a slight increase in time. In terms of effectiveness, the imager had 8% (6/80) critical errors and the loader had 13% (10/80) critical errors in phase 1. In phase 2, the imager (for VHT pairs) had 14% (11/80) critical errors and the loader (for VHT pairs) had 12% (19/160) critical errors. The average SUS score of the system was 70.25, indicating positive usability. A Kruskal-Wallis analysis demonstrated no significant difference in SUS (H value) scores between genders or users with and without smartphone use experience. VectorCam is a usable system for deskilling the in-field identification of mosquito specimens in rural Uganda. Upcoming design updates will address the concerns of users and observers.

Livestock keeping, mosquitoes and community viewpoints: a mixed methods assessment of relationships between livestock management, malaria vector biting risk and community perspectives in rural Tanzania

BackgroundLivestock keeping is one of the potential factors related to malaria transmission. To date, the impact of livestock keeping on malaria transmission remains inconclusive, as some studies suggest a zooprophylactic effect while others indicate a zoopotentiation effect. This study assessed the impact of livestock management on malaria transmission risks in rural Tanzania. Additionally, the study explored the knowledge and perceptions of residents about the relationships between livestock keeping and malaria transmission risks in a selected village.MethodsIn a longitudinal entomological study in Minepa village, South Eastern Tanzania, 40 households were randomly selected (20 with livestock, 20 without). Weekly mosquito collection was performed from January to April 2023. Indoor and outdoor collections used CDC-Light traps, Prokopack aspirators, human-baited double-net traps, and resting buckets. A subsample of mosquitoes was analysed using PCR and ELISA for mosquito species identification and blood meal detection. Livestock's impact on mosquito density was assessed using negative binomial GLMMs. Additionally, in-depth interviews explored community knowledge and perceptions of the relationship between livestock keeping and malaria transmission risks.ResultsA total of 48,677 female Anopheles mosquitoes were collected. Out of these, 89% were Anopheles gambiae sensu lato (s.l.) while other species were Anopheles funestus s.l., Anopheles pharoensis, Anopheles coustani, and Anopheles squamosus. The findings revealed a statistically significant increase in the overall number of An. gambiae s.l. outdoors (RR = 1.181, 95%CI 1.050–1.862, p = 0.043). Also, there was an increase of the mean number of An. funestus s.l. mosquitoes collected in households with livestock indoors (RR = 2.866, 95%CI: 1.471–5.582, p = 0.002) and outdoors (RR = 1.579,95%CI 1.080–2.865, p = 0.023). The human blood index of Anopheles arabiensis mosquitoes from houses with livestock was less than those without livestock (OR = 0.149, 95%CI 0.110–0.178, p < 0.001). The majority of participants in the in-depth interviews reported a perceived high density of mosquitoes in houses with livestock compared to houses without livestock.ConclusionDespite the potential for zooprophylaxis, this study indicates a higher malaria transmission risk in livestock-keeping communities. It is crucial to prioritize and implement targeted interventions to control vector populations within these communities. Furthermore, it is important to enhance community education and awareness regarding covariates such as livestock that influence malaria transmission.

Molecular characterization of Armigeres Subalbatus from Hyderabad region of Telangana state, India.

The mosquito Armigeres subalbatus (Coquillett, 1898) is a significant vector for Japanese encephalitis infection, and breeds in high organic polluted water. Understanding mosquito diversity and there abundance in relation to mosquito-borne diseases is an important component for public health managers. Though the conventional methods for systematic position of mosquito species by using morphological characteristics is a classical method, but it requires perfect expertise and well preserved specimen. Conversely, the molecular analysis of mitochondrial cytochrome C oxidase subunit I (COI) serves as a gene-centric DNA barcoding approach and offers a promising alternative method for mosquito species identification. The study at hand delves into the morphological characteristics of Armigeres subalbatus were compared with COI- gene to ensure a more dependable verification for identification of mosquito species found in Hyderabad region of Telangana. The 489 base pair amplicons were acquired and deposited into the NCBI Gene Bank nucleotide database under the accession number MG686500. The maximum likelihood tree infers that, the Hyderabad species was diverged from USA and Japan species but had ancestral relationship with Tamil Nadu, Karnataka, Maharastra, Kerala and Goa species. Mitochondrial gene (COI) based DNA barcoding is the most reliable and potential alternative technique to identify the mosquito species.

Loop‐mediated isothermal amplification (LAMP) assays for Aedes and Culex species and evaluation of a simple DNA preparation method for field application

Loop‐mediated isothermal amplification (<scp>LAMP</scp>) assays for <i>Aedes</i> and <i>Culex</i> species and evaluation of a simple <scp>DNA</scp> preparation method for field application

A DNA barcode reference library of Croatian mosquitoes (Diptera: Culicidae): implications for identification and delimitation of species, with notes on the distribution of potential vector species

BackgroundMosquitoes pose a risk to human health worldwide, and correct species identification and detection of cryptic species are the most important keys for surveillance and control of mosquito vectors. In addition to traditional identification based on morphology, DNA barcoding has recently been widely used as a complementary tool for reliable identification of mosquito species. The main objective of this study was to create a reference DNA barcode library for the Croatian mosquito fauna, which should contribute to more accurate and faster identification of species, including cryptic species, and recognition of relevant vector species.MethodsSampling was carried out in three biogeographical regions of Croatia over six years (2017–2022). The mosquitoes were morphologically identified; molecular identification was based on the standard barcoding region of the mitochondrial COI gene and the nuclear ITS2 region, the latter to identify species within the Anopheles maculipennis complex. The BIN-RESL algorithm assigned the COI sequences to the corresponding BINs (Barcode Index Number clusters) in BOLD, i.e. to putative MOTUs (Molecular Operational Taxonomic Units). The bPTP and ASAP species delimitation methods were applied to the genus datasets in order to verify/confirm the assignment of specimens to specific MOTUs.ResultsA total of 405 mosquito specimens belonging to six genera and 30 morphospecies were collected and processed. Species delimitation methods assigned the samples to 31 (BIN-RESL), 30 (bPTP) and 28 (ASAP) MOTUs, with most delimited MOTUs matching the morphological identification. Some species of the genera Culex, Aedes and Anopheles were assigned to the same MOTUs, especially species that are difficult to distinguish morphologically and/or represent species complexes. In total, COI barcode sequences for 34 mosquito species and ITS2 sequences for three species of the genus Anopheles were added to the mosquito sequence database for Croatia, including one individual from the Intrudens Group, which represents a new record for the Croatian mosquito fauna.ConclusionWe present the results of the first comprehensive study combining morphological and molecular identification of most mosquito species present in Croatia, including several invasive and vector species. With the exception of some closely related species, this study confirmed that DNA barcoding based on COI provides a reliable basis for the identification of mosquito species in Croatia.Graphical

Hybrid Machine Learning Approach for Mosquito Species Classification Using Wingbeat Analysis

Effective and precise techniques for mosquito species identification are required as mosquito-borne illnesses continue to pose serious threats to public health across the world. We provide a new hybrid machine-learning technique in this research work for the classification of mosquito species through the Wingbeat analysis. It analyzes the wingbeat of the mosquito species based on which it can identify the mosquito species. This method makes use of deep learning techniques. The hybrid technique attempts to provide robust and dependable classification performance by utilizing a wide range of machine learning methods, such as k-Nearest Neighbors (KNN), Random Forest, Multi-layer Perceptron (MLP), Support Vector Machines (SVM), and Gradient Boosting. To improve feature extraction and normalization, we apply a rigorous set of preprocessing techniques to a large dataset that includes wingbeat recordings from many mosquito species. By means of comprehensive testing and analysis, we prove that our method is effective in correctly detecting mosquito species, exhibiting better results than using separate machine learning algorithms. Our findings demonstrate how deep learning methods may support more conventional machine learning strategies in problems involving the categorization of mosquito species. We also address the implications of our results for ecological research and disease management initiatives, highlighting the significance of precise species identification in vector monitoring and epidemiological investigations.

Using capillary electrophoresis to identify Anopheline species in routine sampling sites.

In the Anopheles genus, various mosquito species are able to transmit the Plasmodium parasites responsible for malaria, while others are non-vectors. In an effort to better understand the biology of Anopheles species and to quantify transmission risk in an area, the identification of mosquito species collected in the field is an essential but problematic task. Morphological identification requires expertise and cannot be checked after processing samples in a destructive treatment, while sequencing of numerous samples is costly. Here, we introduce a method of Species identification via Simple Observation Coupled with Capillary Electrophoresis Technology (SOCCET). This molecular technique of species identification is based on precise determination of ITS2 length combined with a simple visual observation, the colour of mosquito hindleg tip. DNA extracted from field-collected Anopheles mosquitoes was amplified with universal Anopheles ITS2 primers and analysed with a capillary electrophoresis device, which precisely determines the size of the fragments. We defined windows of amplicon sizes combined with fifth hind tarsus colour, which allows discrimination of the major Anopheles species found in our collections. We validated our parameters via Sanger sequencing of ITS2 amplicons. Using the SOCCET method, we characterised the composition of Anopheles populations in five locations of French Guiana, where we detected a total of nine species. Anopheles braziliensis and Anopheles darlingi were detected in four locations each and represented 13 and 67% of our samples, respectively. The SOCCET method can be particularly useful when working with routine sampling sites with a moderate species diversity, that is, when the number of local species is too high to define species-specific primers but low enough to avoid individual ITS2 sequencing. This tool will be of interest to evaluate local malaria transmission risk and this approach may be further implemented for other mosquito genera.

The first complete mitochondrional genome of Anopheles gibbinsi using a skimming sequencing approach.

Mosquitoes belonging to the genus Anopheles are the only vectors of human malaria. Anopheles gibbinsi has been linked to malaria transmission in Kenya, with recent collections in Zambia reporting the mosquito species exhibiting zoophilic and exophilic behavioral patterns with occasional contact with humans. Given the paucity of genetic data, and challenges to identification and molecular taxonomy of the mosquitoes belonging to the Anopheles genus; we report the first complete mitochondrial genome of An. gibbinsi using a genome skimming approach. An Illumina Novaseq 6000 platform was used for sequencing, the length of the mitochondrial genome was 15401 bp, with 78.5% AT content comprised of 37 genes. Phylogenetic analysis by maximum likelihood using concatenation of the 13 protein coding genes demonstrated that An. marshallii was the closest relative based on existing sequence data. This study demonstrates that the skimming approach is an inexpensive and efficient approach for mosquito species identification and concurrent taxonomic rectification, which may be a useful alternative for generating reference sequence data for evolutionary studies among the Culicidae.

Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques

The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50, MobileNetV2, EfficientNet-B0, and ConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably, ResNet50 and EfficientNet-B0 demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively. EfficientNet-B0 demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of this EfficientNet-B0 model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well.

Five years of post-validation surveillance of lymphatic filariasis in Thailand

BackgroundThe World Health Organization (WHO) validated Thailand in 2017 as having eliminated lymphatic filariasis (LF) as a public health problem with recommendations for continued surveillance. This article describes measures and progress made in Thailand with post-validation surveillance (PVS) of LF from 2018 until 2022.MethodsThe implementation unit (IU) is a sub-village in 11 former LF endemic provinces. Human blood surveys are targeted in 10% of IUs each year. In Wuchereria bancrofti areas, filaria antigen test strips (FTS) are used, and in Brugia malayi areas, antibody test kits (Filaria DIAG RAPID) are used. Positive cases are confirmed by thick blood film (TBF) and polymerase chain reaction (PCR). Vector surveys for mosquito species identification and dissection for microfilaria (Mf)/filarial larvae are done in 1% of IUs where human blood surveys are conducted. Human blood surveys using FTS are conducted among migrants in five provinces. Surveillance of cats is done in areas that previously recorded > 1.0% Mf rate among cats. Morbidity management and disability prevention (MMDP) are done every 2 years in LF-endemic areas where chronic disease patients reside.ResultsFrom 2018 to 2022, in a total of 357 IUs in 11 provinces, human blood surveys were conducted in 145 IUs (41%) with an average population coverage of 81%. A total of 22,468 FTS and 27,741 FilariaDIAG RAPID were performed. 27 cases were detected: 3 cases of W. bancrofti in Kanchanaburi province and 24 cases of B. malayi in Narathiwat province. 4 cases of W. bancrofti were detected in two provinces through routine public health surveillance. Vector surveys in 47 IUs detected B. malayi Mf filarial larvae only in Narathiwat province. Chronic LF patients reduced from 114 in 2017 to 76 in 2022. Surveys among 7633 unregistered migrants yielded 12 cases of W. bancrofti. Mf rate among cats in Narathiwat province declined from 1.9% in 2018 to 0.7% in 2022. MMDP assessments revealed gaps in healthcare provider’s management of chronic cases due to staff turnover.ConclusionsIn 2022, after 5 years of PVS, Thailand re-surveyed 41% of its previously endemic IUs and demonstrated ongoing transmission in only one province of Narathiwat, where Mf prevalence is below the WHO provisional transmission threshold of 1%. This study highlights the importance of continued disease surveillance measures and vigilance among health care providers in LF receptive areas.Graphical

Rapid and non-destructive identification of Anopheles gambiae and Anopheles arabiensis mosquito species using Raman spectroscopy via machine learning classification models

BackgroundIdentification of malaria vectors is an important exercise that can result in the deployment of targeted control measures and monitoring the susceptibility of the vectors to control strategies. Although known to possess distinct biting behaviours and habitats, the African malaria vectors Anopheles gambiae and Anopheles arabiensis are morphologically indistinguishable and are known to be discriminated by molecular techniques. In this paper, Raman spectroscopy is proposed to complement the tedious and time-consuming Polymerase Chain Reaction (PCR) method for the rapid screening of mosquito identity.MethodsA dispersive Raman microscope was used to record spectra from the legs (femurs and tibiae) of fresh anaesthetized laboratory-bred mosquitoes. The scattered Raman intensity signal peaks observed were predominantly centered at approximately 1400 cm−1, 1590 cm−1, and 2067 cm−1. These peaks, which are characteristic signatures of melanin pigment found in the insect cuticle, were important in the discrimination of the two mosquito species. Principal Component Analysis (PCA) was used for dimension reduction. Four classification models were built using the following techniques: Linear Discriminant Analysis (LDA), Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and Quadratic Support Vector Machine (QSVM).ResultsPCA extracted twenty-one features accounting for 95% of the variation in the data. Using the twenty-one principal components, LDA, LR, QDA, and QSVM discriminated and classified the two cryptic species with 86%, 85%, 89%, and 93% accuracy, respectively on cross-validation and 79%, 82%, 81% and 93% respectively on the test data set.ConclusionRaman spectroscopy in combination with machine learning tools is an effective, rapid and non-destructive method for discriminating and classifying two cryptic mosquito species, Anopheles gambiae and Anopheles arabiensis belonging to the Anopheles gambiae complex.

An Innovative Deep Learning Method to Diagnose Mosquito-Borne Illnesses in Blood Image Analysis

Introduction: Malaria, an infectious illness carried by the bite of infected mosquitoes, is a significant public health concern, especially in Africa. The management of mosquito-human contact is crucial to mitigate its transmission. Artificial intelligence, including machine learning and deep learning techniques, is being utilized to enhance the diagnosis and identification of mosquito species. This advancement aims to facilitate the development of more efficient control measures.&#x0D; Aims and Objective: To analyze the efficiency of three deep learning models in identifying blood-borne diseases by evaluating the macro and micro picture of blood samples.&#x0D; Method: In this retrospective investigation, three deep learning algorithms, namely Convolutional Neural Networks (CNN), MobileNetV2, and ResNet50, were used to identify mosquito-borne illnesses, focusing on malaria. The research used a dataset of 120 blood samples gathered over one year from the hospital's pathology department. The CNN model streamlines preprocessing with multilayer perceptrons, simplifying malaria component extraction. MobileNetV2, a lightweight network, outperforms others with fewer parameters. Its compact blocks in Dense-MobileNet models minimize constraints and computation expenses. ResNet50 resolves degradation issues with a residual structure, preventing overfitting as hidden layers increase.&#x0D; Results: The study evaluated three deep learning models (CNN, MobileNetV2, and ResNet50) for medical classification. The study also demonstrated improved True Positive Rates as False Positive Rates increased, indicating better accurate identification while controlling false positives. ResNet50 consistently outperformed the other models, showcasing its superior performance. The study revealed high precision scores for all models, classifying "Uninfected" and "Infected" cases. ResNet50 exhibited slightly higher precision, indicating its precision-based superiority. Overall, all models demonstrated vital accuracy, and ResNet50 showed exceptional performance. The study found that ResNet50 performs better in True Positive and False Positive Rates.&#x0D; Conclusion: The study has concluded that ResNet50 has shown the best performance in detecting blood-borne diseases.

Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions

Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.

Morphological variability and identification of common mosquito species (Diptera: Culicidae) in Slovenia

Morphological variability and identification of common mosquito species (Diptera: Culicidae) in Slovenia

Molecular and morphometric differentiation of secondary filariasis vector Coquillettidia mosquitoes (Diptera: Culicidae) in Thailand.

Coquillettidia mosquitoes are important nuisance-biting pests and a vector of brugian filariasis in Thailand. However, comprehensive information about these mosquitoes remains unavailable such as molecular and morphometric differences among species. The lack of vector knowledge on Coquillettidia species could affect future disease control. This study aims to investigate differences in molecular variations based on mitochondrial cytochrome oxidase subunit I (COI) gene and wing geometric traits of three Coquillettidia species, namely Cq. crassipes, Cq. nigrosignata, and Cq. ochracea in Thailand. The results of molecular analyses revealed the differences among three Coquillettidia species. The genetic difference measure based on the Kimura two-parameter model among three Coquillettidia species showed low intraspecific distances (0%-3.05%) and large interspecific distances (10.10%-12.41%). The values of intra- and inter-genetic differences of three Coquillettidia species did not overlap which showed the existence of a barcoding gap indicating the efficiency of the identification based on the COI gene. As with molecular analysis, the landmark-based geometric morphometrics approach based on wing shape analysis indicated three distinct species groups which were supported by the high total performance score of cross-validated classification (97.16%). These results provide the first evidence of taxonomic signal based on molecular and wing geometric differences to support species identification and biological variations of Coquillettidia mosquitoes in Thailand for understanding these rare vector mosquitoes in depth and leading to effective further mosquito control.

Aedes aegypti, Ae. albopictus and Culex quinquefasciatus Adults Found Coexisting in Urban and Semiurban Dwellings of Southern Chiapas, Mexico.

Tapachula, Mexico, a tropical city, is an endemic area for dengue, in addition to several outbreaks in the last decade with chikungunya and zika. As part of the migratory corridor from Central to North America and the risks of scattered infectious diseases that this implies, the identification and distribution of potential disease vectors in and around residential areas are essential in terms of entomological surveillance for the prevention of disease outbreaks. The identification of mosquito species of medical importance coexisting in houses and cemeteries in Tapachula and two semiurban sites in southern Chiapas was investigated. Adult mosquitoes were collected from May to December 2018, resting inside and outside houses and in the tombstones and fallen tree leaves in cemeteries. A total of 10,883 mosquitoes belonging to three vector species were collected across 20 sites; 6738 were from neighborhood houses, of which 55.4% were Culex quinquefasciatus, 41.6% Aedes aegypti, and 2.9% Ae. albopictus. Aedes aegypti was the most common mosquito resting inside houses (56.7%), while Ae. albopictus and Cx. quinquefasciatus were mostly found resting outside houses (75.7%). In the cemeteries, Cx. quinquefasciatus (60.8%) and Ae. albopictus (37.3%) were the most abundant, while Ae. aegypti (1.9%) was the least abundant. This is the first report to identify adults of three major disease vector species coexisting in the domestic environment of urban and semiurban sites and Ae. albopictus adult resting inside of urban houses in Mexico. It would be opportune to consider comprehensive strategies that can be applied in this region to control the three species at the same time and avoid outbreaks of the diseases they transmit.