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

Abstract

Background The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences 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, mid-infrared (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 reflection-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 CO 2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classification 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 identified 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 misclassified as goat, and 2% of goat blood meals misclassified 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-effective, 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.

Assessment of vector-host-pathogen relationships using data mining and machine learning

“… Xiong Y, et al 2018 Prediction, Classification NB, KNN, LR, ERT, GBM, XGB, SVM, RF, MC-SGE Q1, Q3 Transcriptomics 73.2, 85.5, 87.9, 89.4, 90.5, 90.1, 90.2, 88.5, metric of F1 5-fold cross-validation, independent test for testing the generalization ability PSSM-composition features 30,682,021 [49] This study focuses on the best way to use validated effector protein features for effector prediction using three machine learning classifiers, and compares results with those of others to obtain de novo results Esna Ashari Z, et al 2019 Classification, Prediction, Clustering SVM, E-SVM Q2, Q3, Q4, Q5 Transcriptomics, Proteomics 94.05%, 93.64%, and 92.44%, for Models 1, 2, and 3, respectively. 10 fold cross-validation Optimal feature set includes 15 features (i.e, coiled coil domains, hydropath, PSSM composites) 31,146,762 [23] Enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities using Mid-infrared spectroscopy and supervised machine learning . Mwanga, E. P., et al 2019 Prediction, Classification KNN, LR, SVM, NB, RF, XGB, MLP Q1, Q2, Q3, Q4, Q5, Q6 Proteomics, Fluxomics, Metabolomics, Cellomics, Population, Phenomics Final model accuracy on hold-out dataset 98.4% Stratified shuffled split cross-validation Spectra intensities above 0.11 absorbance units 31,778,355 [50] The article is a review of recent applications of ML in infection biology, but also discusses the advantages and drawbacks of different techniques.…”

“…The model used proteomics and metabolomics dataset to reach an accuracy level of 80–95% with a 10-fold cross-validation [22] . In addition, KNN, LR, and SVM were used to assess mosquito blood-feeding histories from multi-OMICs datasets with over 98% accuracy [23] . This knowledge could in turn be useful for identifying anthropophilic species with high potential to transmit pathogens.…”

“…Data pertaining to vector-host-pathogen relationships come in many forms and shapes. For example, these could be omics data [28] , [34] , environmental or clinical samples, or laboratory samples [23] . While some of the surveyed articles address this issue, a number of the articles did not detail the processes they applied to solve said challenge.…”

“…In this review, most of the models present were limited by SB big data lack of scalability. For example, the model in [23] and others we reviewed were not scalable. In addition, lack of scalability causes most studies to choose a small subset of data from one location or study to focus on as they cannot integrate and scale big data from geospatial, omics, or other forms of big data.…”

Dengue Disease Dynamics are Modulated by the Combined Influence of Precipitation and Landscapes: A Machine Learning-based Approach

“…This study aimed to examine the combinatory influence of landscape and climate features on mosquito occurrence and dengue incidence across Metropolitan Manila, the Philippines. We employed some advanced machine learning algorithms due to its growing utilization to explore the influence of landscape features or climate on dengue disease (Carvajal, Viacrusis, et al 2018, Guo, et al 2017, Ong, et al 2017, Chen, et al 2018, Baquero, Santana and Chiaravalloti-Neto 2018 and mosquito occurrence (Mwanga, et al 2019, Jiménez, et al 2019, Früh, et al 2018, Zheng, et al 2019. By selecting important environmental features for RFs, we further examined and described the optimal combination of landscape and climate conditions that influence dengue disease and mosquito occurrence using modelbased (MOB) recursive partitioning.…”