Short report: The effect of preservation methods on predicting mosquito age by near infrared spectroscopy

Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis

Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy

The transmission of dengue (DENV) and Zika (ZIKV) has been continuously increasing worldwide. An efficient arbovirus surveillance system is critical to designing early-warning systems to increase preparedness of future outbreaks in endemic countries. The Near Infrared Spectroscopy (NIRS) is a promising high throughput technique to detect arbovirus infection in Ae. aegypti with remarkable advantages such as cost and time effectiveness, reagent-free, and non-invasive nature over existing molecular tools for similar purposes, enabling timely decision making through rapid detection of potential disease. Our aim was to determine whether NIRS can differentiate Ae. aegypti females infected with either ZIKV or DENV single infection, and those coinfected with ZIKV/DENV from uninfected ones. Using 200 Ae. aegypti females reared and infected in laboratory conditions, the training model differentiated mosquitoes into the four treatments with 100% accuracy. DENV-, ZIKV-, and ZIKV/DENV-coinfected mosquitoes that were used to validate the model could be correctly classified into their actual infection group with a predictive accuracy of 100%, 84%, and 80%, respectively. When compared with mosquitoes from the uninfected group, the three infected groups were predicted as belonging to the infected group with 100%, 97%, and 100% accuracy for DENV-infected, ZIKV-infected, and the co-infected group, respectively. Preliminary lab-based results are encouraging and indicate that NIRS should be tested in field settings to evaluate its potential role to monitor natural infection in field-caught mosquitoes.

Rapid detection of kdr mutation F1534C in Aedes aegypti using recombinase polymerase amplification and lateral flow dipsticks

Insecticide resistance monitoring is essential in assessing the efficacy of vector control measures. However, gold standard PCR-based molecular analyses for insecticide resistance detection are often hindered by time-consuming sample processing, as well as considerable infrastructure and resourcing requirements. In this study, we combined a novel one-step sample preparation reagent with a rapid isothermal molecular test that detects a knock down resistance (kdr) mutation (F1534C) that enables pyrethroid resistance in Aedes aegypti mosquitoes. We trialled the rapid F1534C pyrethroid resistance test using insecticide resistant Ae. aegypti mosquito bodies and compared results to a conventional, allele-specific quantitative PCR (AS-qPCR) coupled with melt curve genotyping in corresponding mosquito heads. From a strain of Ae. aegypti established from an insecticide resistant population in Merida, Mexico (n = 27), all the mosquito bodies (n = 27) tested positive with the rapid F1534C test regardless of whether they were homozygous or heterozygous. To assess diagnostic test specificity, we confirmed that F1534 was not detected in laboratory-reared, fully susceptible Ae. aegypti mosquito bodies (n = 28) using the rapid F1534C test or the conventional AS-qPCR melt curve analysis. All corresponding mosquito heads (n = 28) were homozygous wild-type FF1534. The rapid F1534C test thus demonstrated 100% diagnostic sensitivity (95% CI: 87.23% to 100%) and 100% diagnostic specificity (95% CI: 87.66% to 100.00%) for detection of the F1534C pyrethroid resistant single nucleotide polymorphism (SNP) in both heterozygous and homozygous Ae. aegypti. In the collection of mutant mosquitoes from Mexico, CC1534 homozygous mutants occurred at a frequency of 74.1% (n = 20) and FC heterozygous mutants at a frequency of 25.9% (n = 7). The rapid F1534C test significantly reduced the sample processing and testing time from approximately 6 h for the AS-qPCR melt curve analysis to only 25 min. These results demonstrate significant potential for our approach to resistance testing as a field-based, low-resource, rapid alternative to time-consuming and expensive laboratory-based detection.

Adapting field-mosquito collection techniques in a perspective of near-infrared spectroscopy implementation

BACKGROUND

Near-infrared spectroscopy (NIRS) has the potential to be a useful tool for assessing key entomological parameters of malaria-transmitting mosquitoes, including age, infectious status and species identity. However, before NIRS can be reliably used in the field at scale, methods for killing mosquitoes and conserving samples prior to NIRS scanning need to be further optimized. Historically, mosquitoes used in studies have been killed with chloroform, although this approach is not without health hazards and should not be used in human dwellings. For the application of NIRS scanning it is also unclear which mosquito preservation method to use. The aim of the study reported here was to investigate the use of pyrethrum spray, a commercially available insecticide spray in Burkina Faso, for killing mosquitoes METHODS: Laboratory-reared Anopheles gambiae and Anopheles coluzzii were killed using either a pyrethrum insecticide spray routinely used in studies involving indoor mosquito collections (Kaltox Paalga®; Saphyto, Bobo-Dioulasso, Burkina Faso) or chloroform ("gold standard"). Preservative methods were also investigated to determine their impact on NIRS accuracy in predicting the species of laboratory-reared Anopheles and wild-caught mosquito species. After analysis of fresh samples, mosquitoes were stored in 80% ethanol or in silica gel for 2 weeks and re-analyzed by NIRS. In addition, experimentally infected An. coluzzii and wild-caught An. gambiae sensu lato (s.l.) were scanned as fresh samples to determine whether they contained sporozoites, then stored in the preservatives mentioned above for 2 weeks before being re-analyzed.

RESULTS

The difference in the accuracy of NIRS to differentiate between laboratory-reared An. gambiae mosquitoes and An. coluzzii mosquitoes killed with either insecticide (90%) or chloroform (92%) was not substantial. NIRS had an accuracy of 90% in determining mosquito species for mosquitoes killed with chloroform and preserved in ethanol or silica gel. The accuracy was the same when the pyrethrum spray was used to kill mosquitoes followed by preservation in silica gel, but was lower when ethanol was used as a preservative (80%). Regarding infection status, NIRS was able to differentiate between infected and uninfected mosquitoes, with a slightly lower accuracy for both laboratory and wild-caught mosquitoes preserved in silica gel or ethanol.

CONCLUSIONS

The results show that NIRS can be used to classify An. gambiae s.l. species killed by pyrethrum spray with no loss of accuracy. This insecticide may have practical advantages over chloroform for the killing of mosquitoes in NIRS analysis.

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.

Microbiota identified from preserved Anopheles

Background

Mosquito species from the Anopheles gambiae complex and the Anopheles funestus group are dominant African malaria vectors. Mosquito microbiota play vital roles in physiology and vector competence. Recent research has focused on investigating the mosquito microbiota, especially in wild populations. Wild mosquitoes are preserved and transported to a laboratory for analyses. Thus far, microbial characterization post-preservation has been investigated in only Aedes vexans and Culex pipiens. Investigating the efficacy of cost-effective preservatives has also been limited to AllProtect reagent, ethanol and nucleic acid preservation buffer. This study characterized the microbiota of African Anopheles vectors: Anopheles arabiensis (member of the An. gambiae complex) and An. funestus (member of the An. funestus group), preserved on silica desiccant and RNAlater^® solution.

Methods

Microbial composition and diversity were characterized using culture-dependent (midgut dissections, culturomics, MALDI-TOF MS) and culture-independent techniques (abdominal dissections, DNA extraction, next-generation sequencing) from laboratory (colonized) and field-collected mosquitoes. Colonized mosquitoes were either fresh (non-preserved) or preserved for 4 and 12 weeks on silica or in RNAlater^®. Microbiota were also characterized from field-collected An. arabiensis preserved on silica for 8, 12 and 16 weeks.

Results

Elizabethkingia anophelis and Serratia oryzae were common between both vector species, while Enterobacter cloacae and Staphylococcus epidermidis were specific to females and males, respectively. Microbial diversity was not influenced by sex, condition (fresh or preserved), preservative, or preservation time-period; however, the type of bacterial identification technique affected all microbial diversity indices.

Conclusions

This study broadly characterized the microbiota of An. arabiensis and An. funestus. Silica- and RNAlater^®-preservation were appropriate when paired with culture-dependent and culture-independent techniques, respectively. These results broaden the selection of cost-effective methods available for handling vector samples for downstream microbial analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03754-7.

The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence

CONCLUSIONS/SIGNIFICANCE

The potential of RS as a surveillance tool for malaria and arbovirus vectors and MIRS for the diagnosis and surveillance of arboviruses is yet to be assessed. NIRS capacity as a surveillance tool for malaria and arbovirus vectors should be validated under field conditions, and its potential as a diagnostic tool for malaria and arboviruses needs to be evaluated. It is recommended that all 3 techniques evaluated simultaneously using multiple machine learning techniques in multiple epidemiological settings to determine the most accurate technique for each application. Prior to their field application, a standardised protocol for spectra collection and data analysis should be developed. This will harmonise their application in multiple field settings allowing easy and faster integration into existing disease control platforms. Ultimately, development of rapid and cost-effective point-of-care diagnostic tools for malaria and arboviruses based on spectroscopy techniques may help combat current and future outbreaks of these infectious diseases.

High throughput estimates of Wolbachia, Zika and chikungunya infection in Aedes aegypti by near-infrared spectroscopy to improve arbovirus surveillance

Deployment of Wolbachia to mitigate dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) transmission is ongoing in 12 countries. One way to assess the efficacy of Wolbachia releases is to determine invasion rates within the wild population of Aedes aegypti following their release. Herein we evaluated the accuracy, sensitivity and specificity of the Near Infrared Spectroscopy (NIRS) in estimating the time post death, ZIKV-, CHIKV-, and Wolbachia-infection in trapped dead female Ae. aegypti mosquitoes over a period of 7 days. Regardless of the infection type, time post-death of mosquitoes was accurately predicted into four categories (fresh, 1 day old, 2–4 days old and 5–7 days old). Overall accuracies of 93.2, 97 and 90.3% were observed when NIRS was used to detect ZIKV, CHIKV and Wolbachia in dead Ae. aegypti female mosquitoes indicating NIRS could be potentially applied as a rapid and cost-effective arbovirus surveillance tool. However, field data is required to demonstrate the full capacity of NIRS for detecting these infections under field conditions.

Santos et al. demonstrate that the Near Infrared Spectroscopy (NIRS) can accurately estimate the death time of trapped female Aedes aegypti and vector infection with Zika virus, Chikungunya virus, or Wolbachia in a 7-day trapping period. This study suggests that NIRS may provide an accurate and inexpensive tool that improves arbovirus surveillance systems.

An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra

After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions

Background

Practical, field-ready age-grading tools for mosquito vectors of disease are urgently needed because of the impact that daily survival has on vectorial capacity. Previous studies have shown that near-infrared spectroscopy (NIRS), in combination with chemometrics and predictive modeling, can forecast the age of laboratory-reared mosquitoes with moderate to high accuracy. It remains unclear whether the technique has utility for identifying shifts in the age structure of wild-caught mosquitoes. Here we investigate whether models derived from the laboratory strain of mosquitoes can be used to predict the age of mosquitoes grown from pupae collected in the field.

Methods

NIRS data from adult female Aedes albopictus mosquitoes reared in the laboratory (2, 5, 8, 12 and 15 days-old) were analysed against spectra from mosquitoes emerging from wild-caught pupae (1, 7 and 14 days-old). Different partial least squares (PLS) regression methods trained on spectra from laboratory mosquitoes were evaluated on their ability to predict the age of mosquitoes from more natural environments.

Results

Models trained on spectra from laboratory-reared material were able to predict the age of other laboratory-reared mosquitoes with moderate accuracy and successfully differentiated all day 2 and 15 mosquitoes. Models derived with laboratory mosquitoes could not differentiate between field-derived age groups, with age predictions relatively indistinguishable for day 1–14. Pre-processing of spectral data and improving the PLS regression framework to avoid overfitting can increase accuracy, but predictions of mosquitoes reared in different environments remained poor. Principal components analysis confirms substantial spectral variations between laboratory and field-derived mosquitoes despite both originating from the same island population.

Conclusions

Models trained on laboratory mosquitoes were able to predict ages of laboratory mosquitoes with good sensitivity and specificity though they were unable to predict age of field-derived mosquitoes. This study suggests that laboratory-reared mosquitoes do not capture enough environmental variation to accurately predict the age of the same species reared under different conditions. Further research is needed to explore alternative pre-processing methods and machine learning techniques, and to understand factors that affect absorbance in mosquitoes before field application using NIRS.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks

Background

Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier.

Methods and findings

We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers.

Conclusion

We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis. mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

First report on the application of near-infrared spectroscopy to predict the age of Aedes albopictus Skuse

To date, no methodology has been described for predicting the age of Aedes albopictus Skuse mosquitoes, commonly known as Asian tiger mosquitoes. In this study, we report the potential of near-infrared spectroscopy (NIRS) technique for characterizing the age of female laboratory reared Ae. albopictus. Using leave-one-out cross-validation analysis on a training set, laboratory reared mosquitoes preserved in RNAlater for up to a month were assessed at 1, 3, 7, 9, 13, 16, 20 and 25 days post emergence. Mosquitoes (N = 322) were differentiated into two age classes (< or ≥ 7 days) with 93% accuracy, into three age classes (<7, 7-13 and >13 days old) with 76% accuracy, and on a continuous age scale to within ±3 days of their actual average age. Similarly, models predicted mosquitoes (N = 146) excluded from the training model with 94% and 71% accuracy to the two and the three age groups, respectively. We show for the first time that NIRS, with an improved spectrometer and fibre configuration, can be used to predict the age of laboratory reared female Ae. albopictus. Characterization of the age of Ae. albopictus populations is crucial for determining the efficacy of vector control interventions that target their survival.

Monitoring the Age of Mosquito Populations Using Near-Infrared Spectroscopy

Mosquito control with bednets, residual sprays or fumigation remains the most effective tool for preventing vector-borne diseases such as malaria, dengue and Zika, though there are no widely used entomological methods for directly assessing its efficacy. Mosquito age is the most informative metric for evaluating interventions that kill adult mosquitoes but there is no simple or reliable way of measuring it in the field. Near-Infrared Spectroscopy (NIRS) has been shown to be a promising, high-throughput method that can estimate the age of mosquitoes. Currently the ability of NIRS to measure mosquito age is biased, and has relatively high individual mosquito measurement error, though its capacity to rigorously monitor mosquito populations in the field has never been assessed. In this study, we use machine learning methods from the chemometric literature to generate more accurate, unbiased estimates of individual mosquito age. These unbiased estimates produce precise population-level measurements, which are relatively insensitive to further increases in NIRS accuracy when feasible numbers of mosquitoes are sampled. The utility of NIRS to directly measure the impact of pyrethroid resistance on mosquito control is illustrated, showing how the technology has potential as a highly valuable tool for directly assessing the efficacy of mosquito control interventions.

Near-Infrared Spectroscopy, a Rapid Method for Predicting the Age of Male and Female Wild-Type and Wolbachia Infected Aedes aegypti

Estimating the age distribution of mosquito populations is crucial for assessing their capacity to transmit disease and for evaluating the efficacy of available vector control programs. This study reports on the capacity of the near-infrared spectroscopy (NIRS) technique to rapidly predict the ages of the principal dengue and Zika vector, Aedes aegypti. The age of wild-type males and females, and males and females infected with wMel and wMelPop strains of Wolbachia pipientis were characterized using this method. Calibrations were developed using spectra collected from their heads and thoraces using partial least squares (PLS) regression. A highly significant correlation was found between the true and predicted ages of mosquitoes. The coefficients of determination for wild-type females and males across all age groups were R2 = 0.84 and 0.78, respectively. The coefficients of determination for the age of wMel and wMelPop infected females were 0.71 and 0.80, respectively (P< 0.001 in both instances). The age of wild-type female Ae. aegypti could be identified as < or ≥ 8 days old with an accuracy of 91% (N = 501), whereas female Ae. aegypti infected with wMel and wMelPop were differentiated into the two age groups with an accuracy of 83% (N = 284) and 78% (N = 229), respectively. Our results also indicate NIRS can distinguish between young and old male wild-type, wMel and wMelPop infected Ae. aegypti with accuracies of 87% (N = 253), 83% (N = 277) and 78% (N = 234), respectively. We have demonstrated the potential of NIRS as a predictor of the age of female and male wild-type and Wolbachia infected Ae. aegypti mosquitoes under laboratory conditions. After field validation, the tool has the potential to offer a cheap and rapid alternative for surveillance of dengue and Zika vector control programs.

An improved mosquito electrocuting trap that safely reproduces epidemiologically relevant metrics of mosquito human-feeding behaviours as determined by human landing catch

Background

Reliable quantification of mosquito host—seeking behaviours is required to determine the efficacy of vector control methods. For malaria, the gold standard approach remains the risky human landing catch (HLC). Here compare the performance of an improved prototype of the mosquito electrocuting grid trap (MET) as a safer alternative with HLC for measuring malaria vector behaviour in Dar es Salaam, Tanzania.

Methods

Mosquito trapping was conducted at three sites within Dar es Salaam representing a range of urbanicity over a 7-month period (December 2012–July 2013, 168 sampling nights). At each site, sampling was conducted in a block of four houses, with two houses being allocated to HLC and the other to MET on each night of study. Sampling was conducted both indoors and outdoors (from 19:00 to 06:00 each night) at all houses, with trapping method (HLC and MET) being exchanged between pairs of houses at each site using a crossover design.

Results

The MET caught significantly more Anopheles gambiae sensu lato than the HLC, both indoors (RR [95 % confidence interval (CI)]) = 1.47 [1.23–1.76], P < 0.0001 and outdoors = 1.38 [1.14–1.67], P < 0.0001). The sensitivity of MET compared with HLC did not detectably change over the course of night for either An. gambiae s.l. (OR [CI]) = 1.01 [0.94–1.02], P = 0.27) or Culex spp. (OR [CI]) = 0.99 [0.99–1.0], P = 0.17) indoors and declined only slightly outdoors: An. gambiae s.l. (OR [CI]) = 0.92 [0.86–0.99], P = 0.04), and Culex spp. (OR [CI]) = 0.99 [0.98–0.99], P = 0.03). MET-based estimates of the proportions of mosquitoes caught indoors (P_i) or during sleeping hours (P_fl), as well as the proportion of human exposure to bites that would otherwise occurs indoors (π_i), were statistically indistinguishable from those based on HLC for An. gambiae s.l. (P = 0.43, 0.07 and 0.48, respectively) and Culex spp. (P = 0.76, 0.24 and 0.55, respectively).

Conclusions

This improved MET prototype is highly sensitive tool that accurately quantifies epidemiologically-relevant metrics of mosquito biting densities, behaviours and human exposure distribution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1513-1) contains supplementary material, which is available to authorized users.

Estimating malaria transmission from humans to mosquitoes in a noisy landscape

A basic quantitative understanding of malaria transmission requires measuring the probability a mosquito becomes infected after feeding on a human. Parasite prevalence in mosquitoes is highly age-dependent, and the unknown age-structure of fluctuating mosquito populations impedes estimation. Here, we simulate mosquito infection dynamics, where mosquito recruitment is modelled seasonally with fractional Brownian noise, and we develop methods for estimating mosquito infection rates. We find that noise introduces bias, but the magnitude of the bias depends on the ‘colour' of the noise. Some of these problems can be overcome by increasing the sampling frequency, but estimates of transmission rates (and estimated reductions in transmission) are most accurate and precise if they combine parity, oocyst rates and sporozoite rates. These studies provide a basis for evaluating the adequacy of various entomological sampling procedures for measuring malaria parasite transmission from humans to mosquitoes and for evaluating the direct transmission-blocking effects of a vaccine.

Rapid and Non-destructive Detection and Identification of Two Strains of Wolbachia in Aedes aegypti by Near-Infrared Spectroscopy

The release of Wolbachia infected mosquitoes is likely to form a key component of disease control strategies in the near future. We investigated the potential of using near-infrared spectroscopy (NIRS) to simultaneously detect and identify two strains of Wolbachia pipientis (wMelPop and wMel) in male and female laboratory-reared Aedes aegypti mosquitoes. Our aim is to find faster, cheaper alternatives for monitoring those releases than the molecular diagnostic techniques that are currently in use. Our findings indicate that NIRS can differentiate females and males infected with wMelPop from uninfected wild type samples with an accuracy of 96% (N = 299) and 87.5% (N = 377), respectively. Similarly, females and males infected with wMel were differentiated from uninfected wild type samples with accuracies of 92% (N = 352) and 89% (N = 444). NIRS could differentiate wMelPop and wMel transinfected females with an accuracy of 96.6% (N = 442) and males with an accuracy of 84.5% (N = 443). This non-destructive technique is faster than the standard polymerase chain reaction diagnostic techniques. After the purchase of a NIRS spectrometer, the technique requires little sample processing and does not consume any reagents.

Near infrared spectroscopy (NIRS) is a technique that measures specific frequencies of light absorbed by C-H, O-H, S-H and N-H functional groups. Mosquito samples are grouped based upon absorption differences between their chemical properties. In this study, we used NIRS to differentiate 1) Aedes aegypti infected with either of the two strains of intracellular bacterium Wolbachia (wMel and wMelPop) from wild type Ae. aegypti and 2) Aedes aegypti infected with wMel from those infected with wMelPoP. NIRS facilitated the differentiation of wMel and wMelPop from wild type samples and samples infected with either of the Wolbachia infected strains with high prediction accuracies over their lifespan. Predictive models were derived from initial calibration data sets and validated against independent cohorts. Prediction accuracies were high (82–98%) regardless of the cohort mosquitoes were sampled from. The results show that NIRS may have real potential as an alternative method for monitoring Wolbachia incidence in mosquitoes. A rapid, simple and cost-effective surveillance tool suitable for resource-poor areas and large urban release programs would be of great utility for evaluating Wolbachia-based interventions. The models developed during this study require further validation using field collections.

A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species

Species identification-of importance for most biological disciplines-is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond.

The Influence of Diet on the Use of Near-Infrared Spectroscopy to Determine the Age of Female Aedes aegypti Mosquitoes

Interventions targeting adult mosquitoes are used to combat transmission of vector-borne diseases, including dengue. Without available vaccines, targeting the primary vector, Aedes aegypti, is essential to prevent transmission. Older mosquitoes (≥ 7 days) are of greatest epidemiological significance due to the 7-day extrinsic incubation period of the virus. Age-grading of female mosquitoes is necessary to identify post-intervention changes in mosquito population age structure. We developed models using near-infrared spectroscopy (NIRS) to age-grade adult female Ae. aegypti. To determine if diet affects the ability of NIRS models to predict age, two identical larval groups were fed either fish food or infant cereal. Adult females were separated and fed sugar water ± blood, resulting in four experimental groups. Females were killed 1, 4, 7, 10, 13, or 16 days postemergence. The head/thorax of each mosquito was scanned using a near-infrared spectrometer. Scans from each group were analyzed, and multiple models were developed using partial least squares regression. The best model included all experimental groups, and positively predicted the age group (< or ≥ 7 days) of 90.2% mosquitoes. These results suggest both larval and adult diets can affect the ability of NIRS models to accurately assign age categories to female Ae. aegypti.

Evaluating preservation methods for identifying Anopheles gambiae s.s. and Anopheles arabiensis complex mosquitoes species using near infra-red spectroscopy

Background

Near-infrared spectroscopy (NIRS) has been successfully used on fresh and RNAlater®-preserved members of the Anopheles gambiae complex to identify sibling species and age. No preservation methods other than using RNAlater® have been tested to preserve mosquitoes for species identification using NIRS. However, RNAlater® is not the most practical preservative for field settings because it is expensive, requires basic laboratory conditions for storage and is not widely available in sub-Saharan Africa. The aim of this study was to test several cheaper and more field-friendly preservation methods for identifying sibling species of the An. gambiae complex using NIRS.

Methods

In this study we describe the use of NIRS to identify sibling species of preserved An. gambiae s. s. and An. arabiensis. Mosquitoes of each species were placed in sample tubes and preserved using one of the following preservation methods: (i) refrigeration at 4°C, (ii) freezing at −20°C, (iii) drying over a silica-gel desiccant, (iv) submersion in RNAlater® at room temperature, (v) submersion in RNAlater® at 4°C, and (vi) submersion in RNAlater® at −20°C. Mosquitoes were preserved for 1, 4, 10, 32 or 50 weeks before they were scanned.

Results

Storage at 4°C was the only preservation method that, up to 32 weeks, did not result in significantly lower predicted values than those obtained from fresh insects. After 50 weeks, however, refrigerated samples did not give meaningful results. When storing for 50 weeks, desiccating samples over silica gel was the best preservation method, with a partial least squares regression cross-validation of >80%. Predictive data values were analyzed using a generalized linear model.

Conclusion

NIRS can be used to identify species of desiccated Anopheles gambiae s.s. and Anopheles arabiensis for up to 50 weeks of storage with more than 80% accuracy.

Using a near-infrared spectrometer to estimate the age of anopheles mosquitoes exposed to pyrethroids

We report on the accuracy of using near-infrared spectroscopy (NIRS) to predict the age of Anopheles mosquitoes reared from wild larvae and a mixed age-wild adult population collected from pit traps after exposure to pyrethroids. The mosquitoes reared from wild larvae were estimated as <7 or ≥7 d old with an overall accuracy of 79%. The age categories of Anopheles mosquitoes that were not exposed to the insecticide papers were predicted with 78% accuracy whereas the age categories of resistant, susceptible and mosquitoes exposed to control papers were predicted with 82%, 78% and 79% accuracy, respectively. The ages of 85% of the wild-collected mixed-age Anopheles were predicted by NIRS as ≤8 d for both susceptible and resistant groups. The age structure of wild-collected mosquitoes was not significantly different for the pyrethroid-susceptible and pyrethroid-resistant mosquitoes (P = 0.210). Based on these findings, NIRS chronological age estimation technique for Anopheles mosquitoes may be independent of insecticide exposure and the environmental conditions to which the mosquitoes are exposed.