Evaluation of an ultraviolet LED trap for catching Anopheles and Culex mosquitoes in south-eastern Tanzania

Background: Improved surveillance techniques are required to accelerate efforts against major arthropod-borne diseases such as malaria, dengue, filariasis, Zika and yellow-fever. Light-emitting diodes (LEDs) are increasingly used in mosquito traps because they improve energy efficiency and battery longevity relative to incandescent bulbs. This study evaluated the efficacy of a new ultraviolet LED trap (Mosclean) against standard mosquito collection methods. Methods: The study was conducted in controlled semi-field settings and in field conditions in rural south-eastern Tanzania. The Mosclean trap was compared to commonly used techniques, namely CDC-light traps, human landing catches (HLCs), BG-Sentinel traps and Suna traps. Results: When simultaneously placed inside the same semi-field chamber, the Mosclean trap caught twice as many Anopheles arabiensis as the CDC-light trap, and equal numbers to HLCs. Similar results were obtained when traps were tested individually in the chambers. Under field settings, Mosclean traps caught equal numbers of An. arabiensis and twice as many Culex mosquitoes as CDC-light traps. It was also better at trapping malaria vectors compared to both Suna and BG-Sentinel traps, and was more efficient in collecting mosquitoes indoors than outdoors. The majority of An. arabiensis females caught by Mosclean traps were parous (63.6%) and inseminated (89.8%). In comparison, the females caught by CDC-light traps were 43.9% parous and 92.8% inseminated. Conclusions: The UV LED trap (Mosclean trap) was efficacious for sampling Anopheles and Culex mosquitoes. Its efficacy was comparable to and in some instances better than traps commonly used for vector surveillance. The Mosclean trap was more productive in sampling mosquitoes indoors compared to outdoors. The trap can be used indoors near human-occupied nets, or outdoors, in which case additional CO2 improves catches. We conclude that this trap may have potential for mosquito surveillance. However, we recommend additional field tests to validate these findings in multiple settings and to assess the potential of LEDs to attract non-target organisms, especially outdoors

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

Background: The propensity of diferent Anopheles mosquitoes to bite humans instead of other vertebrates infuences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, midinfrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. Methods: Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total refection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm−1 to 400 cm−1 ). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classifcation algorithms were trained using 90% of the spectra through several combinations of 75–25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. Results: The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identifed 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassifed as goat, and 2% of goat blood meals misclassifed as human. Conclusion: Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-efective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries

Evaluation of a push–pull system consisting of transfluthrin-treated eave ribbons and odour-baited traps for control of indoor- and outdoor-biting malaria vectors

Background: Push–pull strategies have been proposed as options to complement primary malaria prevention tools, indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs), by targeting particularly early-night biting and outdoor-biting mosquitoes. This study evaluated different configurations of a push–pull system consisting of spatial repellents [transfluthrin-treated eave ribbons (0.25 g/m2 ai)] and odour-baited traps (CO2-baited BG-Malaria traps), against indoor-biting and outdoor-biting malaria vectors inside large semi-field systems. Methods: Two experimental huts were used to evaluate protective efficacy of the spatial repellents (push-only), traps (pull-only) or their combinations (push–pull), relative to controls. Adult volunteers sat outdoors (1830 h–2200 h) catching mosquitoes attempting to bite them (outdoor-biting risk), and then went indoors (2200 h–0630 h) to sleep under bed nets beside which CDC-light traps caught host-seeking mosquitoes (indoor-biting risk). Number of traps and their distance from huts were varied to optimize protection, and 500 laboratory-reared Anopheles arabiensis released nightly inside the semi-field chambers over 122 experimentation nights. Results: Push-pull offered higher protection than traps alone against indoor-biting (83.4% vs. 35.0%) and outdoor-biting (79% vs. 31%), but its advantage over repellents alone was non-existent against indoor-biting (83.4% vs. 81%) and modest for outdoor-biting (79% vs. 63%). Using two traps (1 per hut) offered higher protection than either one trap (0.5 per hut) or four traps (2 per hut). Compared to original distance (5 m from huts), efficacy of push–pull against indoor-biting peaked when traps were 15 m away, while efficacy against outdoor-biting peaked when traps were 30 m away. Conclusion: The best configuration of push–pull comprised transfluthrin-treated eave ribbons plus two traps, each at least 15 m from huts. Efficacy of push–pull was mainly due to the spatial repellent component. Adding odour-baited traps slightly improved personal protection indoors, but excessive trap densities increased exposure near users outdoors. Given the marginal efficacy gains over spatial repellents alone and complexity of push–pull, it may be prudent to promote just spatial repellents alongside existing interventions, e.g. LLINs or non-pyrethroid IRS. However, since both transfluthrin and traps also kill mosquitoes, and because transfluthrin can inhibit blood-feeding, field studies should be done to assess potential community-level benefits that push–pull or its components may offer to users and non-users

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Background: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance

Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus

Background Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status. Methods Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1–16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection–Fourier transform infrared (ATR–FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1–9 days (young, non-infectious) and 10–16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories. Results The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories. Conclusions This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations

Eave ribbons treated with transfluthrin can protect both users and non-users against malaria vectors

Eave ribbons treated with spatial repellents effectively prevent human exposure to outdoor-biting and indoor-biting malaria mosquitoes, and could constitute a scalable and low-cost supplement to current interventions, such as insecticide-treated nets (ITNs). This study measured protection afforded by transfluthrin-treated eave ribbons to users (personal and communal protection) and non-users (only communal protection), and whether introducing mosquito traps as additional intervention influenced these benefits.; Five experimental huts were constructed inside a 110 m long, screened tunnel, in which 1000 Anopheles arabiensis were released nightly. Eave ribbons treated with 0.25 g/m; 2; transfluthrin were fitted to 0, 1, 2, 3, 4 or 5 huts, achieving 0, 20, 40, 60, 80 and 100% coverage, respectively. Volunteers sat near each hut and collected mosquitoes attempting to bite them from 6 to 10 p.m. (outdoor-biting), then went indoors to sleep under untreated bed nets, beside which CDC-light traps collected mosquitoes from 10 p.m. to 6 a.m. (indoor-biting). Caged mosquitoes kept inside the huts were monitored for 24 h-mortality. Separately, eave ribbons, UV-LED mosquito traps (Mosclean) or both the ribbons and traps were fitted, each time leaving the central hut unfitted to represent non-user households and assess communal protection. Biting risk was measured concurrently in all huts, before and after introducing interventions.; Transfluthrin-treated eave ribbons provided 83% and 62% protection indoors and outdoors respectively to users, plus 57% and 48% protection indoors and outdoors to the non-user. Protection for users remained constant, but protection for non-users increased with eave ribbons coverage, peaking once 80% of huts were fitted. Mortality of mosquitoes caged inside huts with eave ribbons was 100%. The UV-LED traps increased indoor exposure to users and non-users, but marginally reduced outdoor-biting. Combining the traps and eave ribbons did not improve user protection relative to eave ribbons alone.; Transfluthrin-treated eave ribbons protect both users and non-users against malaria mosquitoes indoors and outdoors. The mosquito-killing property of transfluthrin can magnify the communal benefits by limiting unwanted diversion to non-users, but should be validated in field trials against pyrethroid-resistant vectors. Benefits of the UV-LED traps as an intervention alone or alongside eave ribbons were however undetectable in this study. These findings extend the evidence that transfluthrin-treated eave ribbons could complement ITNs

Rapid age-grading and species identification of natural mosquitoes for malaria surveillance

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse An. gambiae, An. arabiensis, and An. coluzzii females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases

Effects of sample preservation methods and duration of storage on the performance of mid-infrared spectroscopy for predicting the age of malaria vectors

Background: Monitoring the biological attributes of mosquitoes is critical for understanding pathogen transmission and estimating the impacts of vector control interventions on the survival of vector species. Infrared spectroscopy and machine learning techniques are increasingly being tested for this purpose and have been proven to accurately predict the age, species, blood-meal sources, and pathogen infections in Anopheles and Aedes mosquitoes. However, as these techniques are still in early-stage implementation, there are no standardized procedures for handling samples prior to the infrared scanning. This study investigated the effects of different preservation methods and storage duration on the performance of mid-infrared spectroscopy for age-grading females of the malaria vector, Anopheles arabiensis. Methods: Laboratory-reared An. arabiensis (N = 3681) were collected at 5 and 17 days post-emergence, killed with ethanol, and then preserved using silica desiccant at 5 °C, freezing at − 20 °C, or absolute ethanol at room temperature. For each preservation method, the mosquitoes were divided into three groups, stored for 1, 4, or 8 weeks, and then scanned using a mid-infrared spectrometer. Supervised machine learning classifiers were trained with the infrared spectra, and the support vector machine (SVM) emerged as the best model for predicting the mosquito ages. Results: The model trained using silica-preserved mosquitoes achieved 95% accuracy when predicting the ages of other silica-preserved mosquitoes, but declined to 72% and 66% when age-classifying mosquitoes preserved using ethanol and freezing, respectively. Prediction accuracies of models trained on samples preserved in ethanol and freezing also reduced when these models were applied to samples preserved by other methods. Similarly, models trained on 1-week stored samples had declining accuracies of 97%, 83%, and 72% when predicting the ages of mosquitoes stored for 1, 4, or 8 weeks respectively. Conclusions: When using mid-infrared spectroscopy and supervised machine learning to age-grade mosquitoes, the highest accuracies are achieved when the training and test samples are preserved in the same way and stored for similar durations. However, when the test and training samples were handled differently, the classification accuracies declined significantly. Protocols for infrared-based entomological studies should therefore emphasize standardized sample-handling procedures and possibly additional statistical procedures such as transfer learning for greater accuracy

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra

Abstract Background Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that Mid-infrared spectroscopy (MIRS) can be used to detect age-specific patterns in mosquito cuticles and thus can be used to train age-grading machine learning models. However, these models tend to transfer poorly across populations. Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS data can improve the transferability of MIRS-based predictions for mosquito ages. Methods We reared adults of the malaria vector Anopheles arabiensis in two insectaries. The heads and thoraces of female mosquitoes were scanned using an attenuated total reflection-Fourier transform infrared spectrometer, which were grouped into two different age classes. The dimensionality of the spectra data was reduced using unsupervised principal component analysis or t-distributed stochastic neighbour embedding, and then used to train deep learning and standard machine learning classifiers. Transfer learning was also evaluated to improve transferability of the models when predicting mosquito age classes from new populations. Results Model accuracies for predicting the age of mosquitoes from the same population as the training samples reached 99% for deep learning and 92% for standard machine learning. However, these models did not generalise to a different population, achieving only 46% and 48% accuracy for deep learning and standard machine learning, respectively. Dimensionality reduction did not improve model generalizability but reduced computational time. Transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate population improved performance to ~ 98% accuracy for predicting mosquito age classes in the alternative population. Conclusion Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies should investigate the optimal quantities and diversity of training data necessary for transfer learning and the implications for broader generalisability to unseen datasets

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning

Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60–100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions