Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.

BACKGROUND: The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays. METHODS AND RESULTS: Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the 'ground truth' for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%-90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI. CONCLUSIONS: This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

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.

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.