Exploring near-infrared spectroscopy ability to predict the age and species of An. gambiae mosquitoes from different environmental conditions in Burkina Faso

doi:10.21203/rs.3.rs-5312047/v1

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Exploring near-infrared spectroscopy ability to predict the age and species of An. gambiae mosquitoes from different environmental conditions in Burkina Faso

https://doi.org/10.21203/rs.3.rs-5312047/v1

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Background

Near infrared spectroscopy (NIRS) showed ability to predict some important entomological parameters in laboratory-reared and wild mosquitoes with moderate to high accuracy. Before validating the technique as a routine tool, it is necessary to assess NIRS accuracy on these variables under different environmental conditions similar to natural setting, as temperature and humidity could impact the mosquito cuticle and interfere with the machine prediction. This study aims to investigate the influence of environmental conditions on NIRS accuracy to determine age and species of An. gambiae s.l.

Methods

Environmental conditions of three important seasonal periods in Burkina Faso covering the onset, the peak and the end of the rainy season were mimicked in the laboratory using incubators. Emerged An. gambiae and An. coluzzii from laboratory colony were reared in each environmental condition and analysed by NIRS to predict mosquito species. Wild An. gambiae s.l. were caught during the 3 different periods described above and analysed by NIRS to compare the two results. Furthermore, first generation of Anopheles was used to assess NIRS ability to determine mosquito age in each environmental condition.

Results

NIRS discriminated between laboratory-reared Anopheles with 83% of accuracy independently of any environmental condition. Similar trend was found in wild-caught Anopheles. NIRS prediction accuracies varied slightly in laboratory Anopheles (77% − 85%) and more strongly in their field counterparts (67% − 84%). In both cases, prediction models developed from the season of interest were a little more accurate than models trained with insectary conditions or from a different period of the year, indicating temperature and humidity can impact NIRS accuracy. Models derived from laboratory-mosquitoes reared under fluctuating environmental conditions predicted field-derived mosquito species with a low accuracy (59%). Models trained on smaller datasets and varying conditions were reliably classified age into two categories (< 9 days or ≥ 9 days, 79% − 84% accuracy).

Conclusion

NIRS was able to predict An. gambiae s.l. species and classified age into two categories under different environmental conditions with modest accuracy. Models trained using wild mosquitoes from one season could predict species in wild mosquitoes from a different season, though with slightly lower accuracy.

Near infrared spectroscopy

Anopheles

Temperature

Relative humidity

Rainy

Dry

Malaria is the most widespread parasitic disease transmitted by Anopheles mosquitoes. Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) have been the most important disease control interventions in endemic countries in Africa [1]. Vector control interventions aim to reduce Anopheles abundance or/and reduce their lifespan to limit Plasmodium transmission [2]. Entomological monitoring is needed to estimate the ability of the mosquito population to transmit the disease and evaluate control interventions [2]. These measurements would benefit from an understanding of the age and species of different Anopheles vector populations. Current methods of estimating the age and species of individual mosquitoes are limited [3, 4] and a variety of complementary or alternative methods are under development [3, 5–7]. Near-infrared spectroscopy (NIRS) has the potential to be an innovative tool in predicting the entomological parameters of malaria. This method has been successfully used in laboratory and field mosquitoes to enable Anopheles species identification [3, 8], age-grading [3, 7–10] and has shown promise for detecting mosquito Plasmodium infection status [11, 12]. Comparatively to other methods, this technique is fast, reagents free, non-destructive, waste free and requires less qualified personnel [4, 6, 11, 12]. While the technique showed promise, there are many other challenges that must be addressed before NIRS can be used as a routine tool in the field [13]. The machine learning models need to be expanded using samples from a much wider diversity of mosquitoes, as models trained with mosquito data from one Anopheles population fail to reliably predict the characteristic of interest in other populations [10]. Models trained with spectra generated from laboratory-reared mosquitoes have been shown to predict field-mosquitoes age with relatively low accuracy [12]. Other studies have reported that the physiological status [14] and diet [15, 16] affect NIRS ability to accurately predict mosquito age. NIRS relies on the property of hydrocarbons within the mosquito cuticle to quantitatively measure changes in organic compounds such as C-H, O-H, N-H, and C-O functional. It detects these changes through the rotation, bending and stretching of these functional groups following exposure to light of the near infrared spectrum. Like any chemical component, the lipids in the arthropod cuticle as in mosquito change in response to rearing conditions [17], inducing the spectra variations in NIRS analyses.

Vector control programs much collect mosquitoes in different locations at different periods of the year. Each of these contexts is characterized by particular environmental conditions such as temperature, relative humidity, photoperiodicity and ecological factors which may be able to induce morphological, physiological and biochemical changes within the mosquitoes. Recent work has reported that Anopheles coluzzii and Anopheles gambiae sensu lato grow faster and live shorter at the peak of the rainy season (August) than their counterparts at the onset of the dry season (December) [18]. Furthermore, mosquitoes of Anopheles gambiae complex reared under conditions typical of the onset of the dry season (December-February) have a larger body size than those reared under conditions from the late dry season (April-May) and those of the peak of the rainy season (August) in the Sahel region [19, 20]. It is thought that An. gambiae reared under dry season conditions appear to have a higher hydrocarbon content compared to rainy season specimens [20]. These same authors reported higher chain lengths in n-alkanes in response to the dry season. These findings suggest seasonality induced cuticular hydrocarbons reorganizations within mosquitoes which could potentially influence NIRS accuracy. This could be a challenge for predicting sample characteristics for a given condition on the basis of models derived from another environmental condition. Verifying the independence of NIRS predictions to environmental conditions is an important step for implementing NIRS technique as a routine diagnostic tool. This study aims to explore NIRS ability to predict species and age-grade An. gambiae s.l. mosquitoes under different environmental conditions in Burkina Faso at three time periods (the onset, peak and end of the rainy season) and compare these to mosquitoes reared under insectary conditions.

Experimental design

This study consisted of evaluating NIRS accuracy to identify species and age-grade of Anopheles gambiae s.l. population reared under different environmental conditions by mimicking the important malaria seasons observed in Burkina Faso. In this country, entomological variations (vector density, mosquito longevity, etc.) occur at three different periods of the year [21]. June period is the onset of the rainy season characterized by significant daily fluctuations in air temperature and a considerable drop in relative humidity. During this period the vectors return from hibernation from reproduction and the malaria transmission repeats. The peak of the rainy season (August) is the second important period associated with low variations of temperature and relative humidity. This period is characterized by a high density of mosquitoes associated with a high transmission of Plasmodium. The third important period is the end of rainy season/onset of the dry season (December) during which important daily fluctuations in air temperature and a significant drop in relative humidity were observed, marks the mosquito aestivation period [22]. Wild Anopheles were sampled at different time points of the year (onset, peak and end of rainy season or onset of dry season) for age determining or species identification using the NIRS technique. An. coluzzii and An. gambiae, used in this study, are closely related species that significantly contribute to malaria transmission in Burkina Faso [23].

Environmental parameters simulation inside the incubator

In the IRSS laboratory, three incubators (Sanyo MLR 315H, Sanyo Electric Co., Osaka, Japan) were used to mimic cyclically environmental conditions of June, August and December for mosquitoes rearing [19, 22]. For each period, hourly temperature average and relative humidity (RH) (Fig. 1) cycles were scheduled inside the climate chambers based on data hourly recorded in the village of Bama in previous work [19]. To mimic natural daily climate fluctuations, a 12-step cycle of temperature and RH was set up in the climate chambers. A photoperiod of light/dark 12:12 hours cycle in all climate chambers was performed for all experiments due to the low variations in photoperiod in the tropics (about 1h 15 minutes day length variation between the solstices in Bobo-Dioulasso, Burkina-Faso). In a fourth incubator, insectary conditions were programmed (temperature 27°C ± 2, relative humidity 70% ± 5 with light/dark 12:12 hours cycle) and acted as a control.

Mosquito rearing inside the incubator for NIRS analysis

Species identification of laboratory An. gambiae s.l.

In the first part of the experiment, we tested how environmental changes interact with NIRS predictions using adult mosquitoes. Laboratory-reared female An. gambiae and An. coluzzii were sourced from an outbred colony established in 2018. Since changes in mosquito cuticles happen during adulthood [20], larvae were developed under controlled conditions (temperature 27°C ± 2, relative humidity 70% ±5 with light/dark 12:12 hours) and then the resulting adult females were exposed under four environmental conditions. Briefly, eggs laid by females of An. gambiae and An. coluzzii were separately hatched in trays containing water in the insectary. The resulting larva were fed Ad libitum with fish food (Tetramin® Baby) and pupae were pipetted into a plastic cup with half-filled spring water and then placed in cages covered with a mosquito untreated net for emergence. Emerged female mosquitoes were organized by groups in cages and then kept in incubators where environmental conditions were programmed as designed below (Fig. 2). Mosquitoes were fed Ad libitun with 10% glucose solution that changed daily. Approximatively 60 adults of An. coluzzii and An. gambiae for each experimental group were sampled at different periods (3, 6, 9, 12, 15 and 18 days post-emergence) for NIRS analysis to identify Anopheles species. Four replicates were performed in this experiment. To avoid any ''effect of the cage'', the positions were changed over time in climate chambers. In addition, climate chambers were also changed between conditions to avoid any interference of the incubator in data analysis.

Species identification of field-derived An. gambiae s.l.

We investigated the influence of different environmental conditions on NIRS accuracy in predicting Anopheles species in the field and compare with laboratory-reared sample predictions. Wild mosquitoes were collected in the villages surrounding Bobo-Dioulasso at the periods with similar conditions to which were reproduce in the incubators: June, July-September, October-November corresponding respectively to the start, the peak and the end of rainy season. For each period, mosquitoes were caught early in the morning in the living rooms dwellings using mouth aspirators [24]. An. gambiae s.l. morphologically identified based on Giles and Coetzee keys [25], were transferred at IRSS insectary in standard conditions (temperature 27 ± 2°C, 70 ± 5% relative humidity, 12:12 light/dark). The unfed Anopheles were immediately scanned by NIRS. After each mosquito scanning, the carcass was analysed for species determination using conventional PCR protocol [26].

Wild- mosquito age determination

First generation (F1) of wild female An. gambiae s.l. were used in this experiment. Blood-engorged An. gambiae s.l. were caught during rainy season early in the morning using a mouth aspirator [24] in the living rooms of human in the villages of Bama and Soumousso. As reported by previous studies [27, 28], almost 99% of Bama’s mosquitoes are An. coluzzii while those from Soumousso include three major’s species (An. gambiae, An. arabiensis and An. coluzzii). After collection, mosquitoes were kept live in cages according to the locality. They were maintained under a sugar meal of 10% glucose solution and submitted to oviposition as described in our previous work [18]. Mosquitoes were killed after egg laying, individually preserved in Eppendorf tubes and then analysed by conventional PCR for species identification [26]. Eggs collected from F0 females of each locality, were organized by pools and then incubated in the different conditions (insectary, August and December) to obtain F1 generation mosquitoes. The adult female mosquitoes were kept in the same conditions, fed with 10% glucose sugar solution and around 30 specimens were sampled every 3 days (from day 3 to day 18) for NIRS analysis. Because of NIRS inability to accurately determine the chronological age of mosquitoes [7], a threshold of 9 days was established to categorize the mosquitoes as either young or old. This threshold was selected to encompass the potential age range for malaria transmission by the mosquito. Two replicates were performed for this experiment.

Mosquito scanning and data analysis

Mosquitoes from each experiment were killed with chloroform vapor and their cephalon-thorax were scanned using a LabSpec4 Standard-Res i (standard resolution, integrated light source) near-infrared spectrometer and a bifurcated reflectance probe mounted 2 mm from a spectral on white reference panel (ASD Inc.). RS3 spectral acquisition software (ASD Inc.) which averages spectra from 20 scans, was used to record absorbance at 2151 wavelengths from 350 to 2500 nm of the electromagnetic spectrum. All specimens were scanned on the two sides focusing the light probe on the head and thorax region.

Data analyses were conducted using R software version 4.0.2. The wavelengths from 700 to 2350 nm were used to remove the excess noise arising from the sensitivity of the spectrometer at the ends of the near-infrared range. PLS (partial least squares regression) was used to analyse these spectra using packages downloaded from https://github.com/pmesperanca/ mlevcm [29]. Therefore, a machine learning approach was used to create and cross-validate the best model using a generalized linear model [12, 29]. Anopheles species identification and age classification (< 9 days or ≥ 9 days) were performed using a binomial logistic classification model. In this analysis, two response classes y = 0 and y = 1 were attributed to An. gambiae and An. coluzzii, respectively. The same procedure was used when classifying ages into two groups: y < 9 days for young mosquitoes and y ≥ 9 days for old mosquitoes. All analyses were performed using simple models without spectra smoothing or penalized estimation of the coefficient function. For reducing the overfitting, data are split into three subsets: 60%, 20% and 20% for training, validation and testing (estimation of the generalized error) the model, respectively. The number of principal components was allowed to vary between 2 and 50. This process was repeated 50 times to reduce errors. The observations were balanced by random sampling of the total number of mosquitoes analysed in all models. Model accuracy is obtained through the value of the area under the receiver operating characteristic (ROC) curve (AUC). A value of AUC close to or equal 1 indicates a good accuracy. The accuracies of the different models were compared using Fisher exact test. As mosquitoes were reared in different conditions, a calibration model developed using samples from one condition was used to predict the species or age of mosquitoes from another condition.

NIRS ability to identify An. gambiae s.l. species from different environmental conditions

A total of 4131 laboratory derived Anopheles of various ages and reared under different environmental conditions were used to assess NIRS accuracy. The binomial logistic classification model trained on mosquitoes reared in all conditions in the laboratory discriminated between An. gambiae and An. coluzzii with 83% of accuracy (An. gambiae = 84%; An. coluzzii = 82%; Fig. 3a). A small difference of accuracy was observed for the wild-caught mosquitoes (accuracy = 84%; Fig. 3b) when predictive model was trained on specimens of all periods. The preliminary analyses already revealed the different profiles of spectra between the two groups of mosquitoes (laboratory-mosquitoes versus field mosquitoes) (Fig. 4).

When a calibration model was generated from laboratory mosquitoes reared in one condition to predict those in other conditions, NIRS accuracies were more variable from 77–85% (Fig. 4a). In most situations models trained with mosquitoes from the same condition were more accurate, i.e. models calibrated with mosquitoes reared under August conditions were the most reliable in predicting mosquito species reared under August conditions. Nevertheless, needs to be taken when interpreting differences as the number of mosquitoes in each model varied substantially (Fig. 2). The lowest accuracy was obtained when models derived from specimens reared under June conditions were used to predict Anopheles species from other rearing conditions. However, these models were also trained on the smallest number of mosquitoes. In contrast, the high accuracy was found in August conditions when model derived from mosquitoes in these conditions were used to predict Anopheles species from another reared conditions. (Fig. 5a). The overall accuracies of different comparisons using a predictive model from one condition to identify mosquito species in all conditions resulted no difference for distinguishing An. gambiae s.l. species (P = 0.11). Altogether, the models derived from insectary conditions and the peak of the rainy season mosquitoes appear appropriate for species predictions under other conditions (Fig. 5a). These observations need to be validated with a large number of mosquitoes in each model.

The analysis was extended to investigate whether these finding could be generalized to wild Anopheles. A total of 4343 mosquitoes with 12.8%, 41.5%, and 45.7% of An. arabiensis, An. gambiae and An. coluzzii respectively were collected in the field and used to train; and test the model. Due to the low sample size of An. arabiensis, only spectra derived from An. gambiae and An. coluzzii were included in the data analysis. Globally, the model developed using mosquitoes of all periods classified An. gambiae and An. coluzzii with 84% of accuracy (An. gambiae, 86%; An. coluzzii, 82%, Fig. 3b). Again, accuracies varied considerably between 67 and 84% when the model trained with samples collected in one period was used to predict samples from another period (Fig. 5b). Indeed, the model derived from mosquitoes at the end of the rainy season generated low accuracy (67%) in predicting onset-season mosquito species, though these models were trained on the smallest number of mosquitoes. The highest accuracy (84%) was found with the model train on samples from the peak of the rainy season. As in laboratory Anopheles, the model trained from the peak of the rainy season specimens seems to be better for the predictions. Finally, models derived from laboratory-mosquitoes reared under fluctuating environmental conditions failed to predict field-derived mosquito species with an accuracy of 59% (An. gambiae, 59%; An. coluzzii, 58%).

NIRS accuracy to predict wild An. gambiae s.l. age under different environmental conditions

Around 2500 wild Anopheles were collected in the field, with 1000 females from Bama and 1500 from Soumousso. As expected, the three major species of Anopheles involved in malaria transmission (51.6% An. gambiae, 38.2% An. arabiensis and 10.2% An. coluzzii) were found in Soumousso while Bama mosquitoes were nearly all An. coluzzii. The first-generation female (F1) of these mosquitoes was used for NIRS analysis. Mosquito ability to transmit Plasmodium required a minimum longevity covering the parasite extrinsic incubation period (EIP) estimate at about 9 days [30]. We investigated the accuracy NIRS technique to predict mosquito age as young (< 9 days) or old (≥ 9 days) under different environmental conditions. Based on 9 days as threshold age for malaria transmission, NIRS accurately classify mosquitoes into young and old with slight difference between environmental conditions. Regardless of mosquito group and environmental conditions, the binomial predictive model indicated that NIRS was able to classify mosquitoes as young (< 9 days) and old (≥ 9 days), with 81% of accuracy for both ages’ classifications. The similar accuracies were found under each environmental condition with slight variations depending on the Anopheles group, range from 79–84% (Fig. 6). Additionally, NIRS classified younger Anopheles (< 9 days old) than their older counterparts whatever environmental conditions and mosquito group (An. coluzzii: insectary conditions (< 9, 84%; ≥ 9, 83%), August conditions (< 9, 82%; ≥ 9, 81%), December conditions (< 9, 83%; ≥ 9, 79%); An. gambiae s.l.: insectary conditions (< 9, 87%; ≥ 9, 80%), August conditions (< 9, 86%; ≥ 9, 79%), December conditions (< 9, 81%; ≥ 9, 77%). These accuracies were obtained by training model on samples of one condition and validating on a subsample of the same condition. However, NIRS was unable to distinguish between young and old mosquitos when we attempted to use the model derived from mosquitoes reared in one environmental condition to predict the age of mosquitoes reared in another condition (< 50% accuracy).

NIRS can successfully discriminate between laboratory-reared and field-collected An. gambiae and An. coluzzii with varying degrees of accuracy depending on the environmental conditions. Overall, this work suggests NIRS was able to distinguish both An. gambiae and An. coluzzii in the laboratory and in the field with greater than 80% of accuracy. Inter-environmental conditions accuracies of species identification vary slightly in laboratory mosquitoes but more in field specimens. Nevertheless, it is encouraging to see that wild mosquitoes caught in one season can broadly predict species in mosquitoes caught months later, though with some loss of accuracy. We hypothesize that high variation in inter-period accuracies could be due to several potential factors: the diversity of breeding sites, nutriments (blood and sugar sources), gonotrophic/physiological status[12] and resistance status to the insecticides of the mosquitoes [29]. Since these factors weren't controlled in the field, the differences in accuracy couldn't be solely attributed to the abiotic factors of various collection periods. Additionally, the spectra from laboratory mosquitoes differ from those of field mosquitoes in this study as reported by other authors [7]. This study underlines once more the risk in extrapolating laboratory findings to the real-life environment, as previously suggested by other authors [31]. Results also revealed that the model derived from peak season samples was better for predicting samples from any other conditions, both in the laboratory and in the field. This period is characterized by a low fluctuation in temperature and RH, which could have less impact on absorbances through the mosquito cuticle compared to other periods. However, these findings need to be confirmed with a significant number of mosquitoes in predictive models. Furthermore, study showed that the machine learning algorithm trained using laboratory-mosquitoes reared under different environmental conditions failed to predict field-derived mosquito species with a good accuracy. The mosquitoes were reared in laboratory under controlled conditions, free from factors like competition, nutrition, parasitism and several other factors that could create differences between laboratory specimens and those in the wild. This may mean that laboratory-reared mosquitoes don’t reflect all the variations found in wild populations, so that predictive models derived from them are able to correctly predict the species of field specimens. On the other hand, findings revealed that NIRS is able to classify both An. coluzzii and An. gambiae s.l. of age < 9 days and ≥ 9 days with good accuracies in each environmental condition. Although this technique is not useful to determine chronological age of mosquitoes, it may provide a rapid means of acquiring an epidemiologically relevant measure of the proportion of the vector population that is old enough to transmit malaria. This is ecologically important because it may be helping to design and monitor vector prevention efforts; and assess the risk of pathogen transmission in the human population. Overall, we found that technique slightly better predicted mosquitoes < 9 days old than their counterparts ≥ 9 days old. This supports the assertion that major biochemical changes detected by NIRS occur in mosquito cuticles from birth and stagnate between days 10 and 14 due to cuticle thickening [32]. Findings indicated that a calibration model derived from mosquitoes of specific environmental condition determined mosquito species of another condition with relatively good accuracy. However, classifying Anopheles age into two groups using a model derived of specimens from another environmental condition was a real challenge, with very low accuracy. This is likely to be driven by the low number of mosquitoes to train the models from some conditions or perhaps the diversity in spectra seen when temperatures fluctuate. While statistical analyses require subsequent sample size to improve model accuracy [10], the number of mosquitoes in our study substantially varied in the different seasons. Therefore, care should be taken interpreting the reliability of different models trained on data from different season. NIRS would be more useful if a calibration model derived from specimens in one environmental condition is able to predict age of Anopheles in another condition. This work suggests temperature and relative humidity could influence mosquito spectra and that future models should include some variability in these factors in the data used to train and validate the models. Similar results were reported by other authors who found that models derived from laboratory mosquitoes were unable to predict the age of field derived mosquitoes reared under ambient condition [7]. Similarly, models derived from one preserved mosquito were unsuitable for predicting the age of specimens stored in another preservative [33]. Further work is needed to improve the algorithms used in NIRS data analysis, so that models derived from mosquitoes of one origin, one geographical area or sampling period can be used to predict entomological parameters of mosquitoes from various origins. Ultimately, NIRS accuracy and out-of-sample prediction is likely to be improved by substantially increasing the number of mosquitoes used in the training and validating datasets. These mosquitoes should be reared in as diverse a set of environmental variables as possible to include the range likely in the wild mosquitoes being tested.

Before using NIRS as a potential tool for determining important entomological parameters, several challenges must be addressed, particularly the identification of mosquito species and age in various environmental contexts. The finding revealed that NIRS is able to predict An. gambiae s.l. species under different environmental conditions with good accuracy. In addition, models derived from An. gambiae s.l. in one environmental condition is able to predict Anopheles species in more other condition with relatively good accuracy. However, models trained using laboratory-mosquitoes reared under various environmental conditions predict field-derived mosquito species with a low accuracy. On the other hand, NIRS provided a good classification of age under all environmental conditions for both An. coluzzii and An. gambiae s.l. when the age was classified into two groups age (< 9 days or ≥ 9 days) using binary classification models. The models derived from Anopheles in one environmental condition failed to predict the age of mosquitoes in any condition. Further studies are needed to investigate any influence of other factors such as the accuracy of NIRS on Anopheles infection status or Anopheles resistance to insecticide status under different environmental conditions in order to develop appropriate calibration of the machine before field application.

NIRS: Near infrared spectroscopy

s.l.: sensu lato

ITNs: Insecticide-treated nets

IRS: Indoor residual spray

RH: Relative humidity

An: Anopheles

PCR: Polymerase chain reaction

PLS: Partial least squares regression

ROC: Receiver operating characteristic curve

AUC: Area under curve

Acknowledgements

The authors thank the residents of Bama, Klesso, Longo and Soumousso for their sincere cooperation during mosquito collection.

Funding

The work was supported by the Centre d’Excellence Africain en Innovations Biotechnologiques pour l’Elimination des Maladies à transmission vectorielle (CEA- ITECH/MTV) of Nazi BONI University, Burkina Faso and UK Medical Research Council (MRC) Project Grant (MR/P01111X/1).

Availability of data and materials

The datasets are fully available without restriction from the corresponding authors by request. Further inquiries can be directed to the corresponding authors.

Author contributions

NDCD, DFD, and RKD conceived and designed the study. NDCD, DFD and BMS conducted the laboratory and field work. NDCD, LIGP and FC conducted the molecular analysis. NDCD, BMS and TSC were responsible for the data analysis. NDCD and DFD wrote the first draft of the manuscript. BMS, FC, JK, TSC and RKD reviewed the manuscript. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

The project received approval from the local institutional ethics committee, reference number: A018-2017/CEIRES.

Consent for publication

“Not applicable” for that section.

Competing interests

The authors declare no competing interest.

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No competing interests reported.

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Exploring near-infrared spectroscopy ability to predict the age and species of An. gambiae mosquitoes from different environmental conditions in Burkina Faso

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Material and methods

Experimental design

Environmental parameters simulation inside the incubator

Mosquito rearing inside the incubator for NIRS analysis

Wild- mosquito age determination

Mosquito scanning and data analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1