Abstract
BackgroundUnderstanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk. The use of near-infrared spectroscopy (NIRS) for age-grading has been demonstrated previously on laboratory and semi-field mosquitoes, but to date has not been utilized on wild-caught mosquitoes whose age is externally validated via parity status or parasite infection stage. In this study, we developed regression and classification models using NIRS on datasets of wild An. gambiae (s.l.) reared from larvae collected from the field in Burkina Faso, and two laboratory strains. We compared the accuracy of these models for predicting the ages of wild-caught mosquitoes that had been scored for their parity status as well as for positivity for Plasmodium sporozoites.ResultsRegression models utilizing variable selection increased predictive accuracy over the more common full-spectrum partial least squares (PLS) approach for cross-validation of the datasets, validation, and independent test sets. Models produced from datasets that included the greatest range of mosquito samples (i.e. different sampling locations and times) had the highest predictive accuracy on independent testing sets, though overall accuracy on these samples was low. For classification, we found that intramodel accuracy ranged between 73.5–97.0% for grouping of mosquitoes into “early” and “late” age classes, with the highest prediction accuracy found in laboratory colonized mosquitoes. However, this accuracy was decreased on test sets, with the highest classification of an independent set of wild-caught larvae reared to set ages being 69.6%.ConclusionsVariation in NIRS data, likely from dietary, genetic, and other factors limits the accuracy of this technique with wild-caught mosquitoes. Alternative algorithms may help improve prediction accuracy, but care should be taken to either maximize variety in models or minimize confounders.
Highlights
Understanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk
While the interval partial least squares (PLS) (iPLS) predictions for datasets 3 and 4 chose the same interval as having the lowest root mean squared error metric (RMSE) value in crossvalidation (740–799 nm), there was no ability of these models to predict each other
Accuracy for cross-validation and validation datasets indicated that there may be some loss in accuracy due to high complexity present in the datasets
Summary
Understanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk. If a mosquito has not yet undergone a gonotrophic cycle (nulliparous), the tracheoles tend to be in tightly coiled “skeins.” if the tracheoles are unraveled, follicular development and oogenesis has likely occurred at least once (parous) This methodology has been utilized widely as it is a relatively simple dissection procedure, though it results in a coarse metric of age because many mosquitoes, An. gambiae, become parous early in life. Few researchers have successfully used this technique due to these limitations [13,14,15], and others have indicated that even when done successfully, the approach is flawed because of the presence of “rogue” ovarioles that indicate a gonotrophic cycle that did not occur [16, 17] These non-diagnostic ovarioles increase in their frequency as the mosquito ages, and can be confounded by taking multiple blood meals between age batches [18, 19]. All dissection approaches are limited by the speed of the dissection, making high throughput processing difficult [20]
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