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To eliminate malaria, scalable tools that are rapid, affordable, and can detect patients with low parasitemia are required. Non-invasive diagnostic tools that are rapid, reagent-free, and affordable would also provide a justifiable platform for testing malaria in asymptomatic patients. However, non-invasive surveillance techniques for malaria remain a diagnostic gap. Here, we show near-infrared Plasmodium absorption peaks acquired non-invasively through the skin using a miniaturized hand-held near-infrared spectrometer. Using spectra from the ear, these absorption peaks and machine learning techniques enabled non-invasive detection of malaria-infected human subjects with varying parasitemia levels in less than 10 s.

BackgroundAfter 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 to 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 currently are used to determine the parity status of mosquitoes, are very tedious and limited to very 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.Methods and resultsIn this study, we train 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 collected from Muleba, Tanzania (Muleba-GA); An. gambiae collected from Burkina Faso (Burkina-GA); and An.gambiae 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 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=927), 68.7 ± 4.8% (N=140), 80.3 ± 2.0% (N=158), and 75.7 ± 2.5% (N=298), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1 ± 2.2%, (N=927), 89.8 ± 1.7% (N=140), 93.3 ± 1.2% (N=158), and 92.7 ± 1.8% (N=298) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively.ConclusionThese results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate 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.

This paper reports the use of near infrared and mid-infrared spectroscopy to detect the presence and quantity of eggs of the gastrointestinal nematode Haemonchus contortus in sheep faeces. Haemonchus contortus eggs were quantified in dried, finely ground sheep faeces and in moist, coarsely ground faeces using near infrared and mid-infrared bench top spectrometers and a portable near infrared spectrometer. When Haemonchus contortus eggs were presented without faecal medium, it was found that the wavelength region of 1880–2100 nm was most important for detection. Broad classes of chemical properties found in the near infrared region were identified for dried Haemonchus contortus eggs using a mid-infrared spectrometer. However, when Haemonchus contortus eggs were mixed into the complex matrix of sheep faeces, the development of a robust calibration model for egg detection proved to be challenging (R2

Spectroscopy-based techniques are emerging diagnostic and surveillance tools for mosquito-borne diseases. This review has consolidated and summarised recent research in the application of Raman and infrared spectroscopy techniques including near- and mid-infrared spectroscopy for malaria and arboviruses, identified knowledge gaps, and recommended future research directions. Full-length peer-reviewed journal articles related to the application of Raman and infrared (near- and mid-infrared) spectroscopy for malaria and arboviruses were systematically searched in PUBMED, MEDILINE, and Web of Science databases using the PRISMA guidelines. In text review of identified studies included the methodology of spectroscopy technique used, data analysis applied, wavelengths used, and key findings for diagnosis of malaria and arboviruses and surveillance of mosquito vectors. A total of 58 studies met the inclusion criteria for our systematic literature search. Although there was an increased application of Raman and infrared spectroscopy-based techniques in the last 10 years, our review indicates that Raman spectroscopy (RS) technique has been applied exclusively for the diagnosis of malaria and arboviruses. The mid-infrared spectroscopy (MIRS) technique has been assessed for the diagnosis of malaria parasites in human blood and as a surveillance tool for malaria vectors, whereas the near-infrared spectroscopy (NIRS) technique has almost exclusively been applied as a surveillance tool for malaria and arbovirus vectors. 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., Author summary Malaria and many arboviruses such as Dengue virus, Zika virus, Chikungunya virus, and Ross River virus are persistent and detrimental to the global population. Rapid and accurate diagnosis of these infections in human populations and mosquito vectors is essential for understanding their epidemiology, for prompt treatment, and to improve and guide control and elimination strategies. Raman and infrared spectroscopy are rapid and cost-effective tools that have shown potential as diagnostic and surveillance tools for malaria and arboviruses. This systematic review presents up-to-date research conducted using RS, MIRS, and NIRS for the diagnosis of malaria parasite and arboviruses as well as for the surveillance of malaria and arbovirus vectors.

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.

BackgroundExisting diagnostic methods for the parasitic gastrointestinal nematode,Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces.MethodsVisible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores.ResultsPrincipal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood.ConclusionThis study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas whereH. contortusis endemic.Graphical Abstract

Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces.Methods: Vis-NIR spectra were obtained from worm-free sheep faeces from different environments in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations collected. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected Queensland (QLD) faeces. Naturally occurring blood in QLD samples was quantified using Hemastix® and FAMACHA© scores.Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions in sheep faeces. The calibration models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood.Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic for the accurate prediction of H. contortus infections in these regions.

Antimicrobial resistance (AMR) caused by Carbapenem-Resistant Enterobacteriaceae (CRE) is a global threat. Accurate identification of these bacterial species with associated AMR is critical for their management. While highly accurate methods to detect CRE are available, they are costly, timely and require expert skills making their application infeasible in low-resource settings. Here, we investigated the potential of Near-infrared Spectroscopy (NIRS) for a range of applications; i) the detection and differentiation of isolates of two pathogenic Enterobacteriaceae species, Klebsiella pneumoniae and Escherichia coli and, ii) the differentiation of carbapenem resistant and susceptible K. pneumoniae. NIRS has successfully differentiated between K. pneumoniae and E. coli isolates with a predictive accuracy of 89.04% (95% CI; 88.7-89.4%). K. pneumoniae isolates harbouring carbapenem resistance determinants were differentiated from susceptible K. pneumoniae strains with an accuracy of 85% (95% CI; 84.2-86.1%). To our knowledge, this is the largest demonstration of a proof of concept for the utility and feasibility of NIRS for rapidly differentiating between K. pneumoniae from E.coli as well as from carbapenem resistant K. pneumoniae from susceptible strains.

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 (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.

BackgroundNear 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 findingsWe 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.ConclusionWe 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.

Background Natural infections of the endosymbiont bacteria Wolbachia have recently been discovered in populations of the malaria mosquito Anopheles gambiae (s.l.) in Burkina Faso and Mali, West Africa. This Anopheles specific strain wAnga limits the malaria parasite Plasmodium falciparum infections in the mosquito, thus it offers novel opportunities for malaria control. Results We investigated Wolbachia presence in Anopheles arabiensis and Anopheles funestus, which are the two main malaria vectors in the Kilombero Valley, a malaria endemic region in south-eastern Tanzania. We found 3.1% (n = 65) and 7.5% (n = 147) wAnga infection prevalence in An. arabiensis in mosquitoes collected in 2014 and 2016, respectively, while no infection was detected in An. funestus (n = 41). Phylogenetic analysis suggests that at least two distinct strains of wAnga were detected, both belonging to Wolbachia supergroup A and B. Conclusions To our knowledge, this is the first confirmation of natural Wolbachia in malaria vectors in Tanzania, which opens novel questions on the ecological and genetic basis of its persistence and pathogen transmission in the vector hosts. Understanding the basis of interactions between Wolbachia, Anopheles mosquitoes and malaria parasites is crucial for investigation of its potential application as a biocontrol strategy to reduce malaria transmission, and assessment of how natural wAnga infections influence pathogen transmission in different ecological settings. Electronic supplementary material The online version of this article (10.1186/s13071-018-3249-y) contains supplementary material, which is available to authorized users.

Antimicrobial Resistance (AMR) caused by Carbapenem-Resistant Enterobacteriaceae (CRE) is a global threat. Accurate identification of these bacterial species with associated AMR is critical for their management. While highly accurate methods to detect CRE are available, they are costly, timely and require expert skills, making their application infeasible in low-resource settings. Here, we investigated the potential of Near-Infrared Spectroscopy (NIRS) for a range of applications: (i) the detection and differentiation of isolates of two pathogenic Enterobacteriaceae species, Klebsiella pneumoniae and Escherichia coli, and (ii) the differentiation of carbapenem resistant and susceptible K. pneumoniae. NIRS has successfully differentiated between K. pneumoniae and E. coli isolates with a predictive accuracy of 89.04% (95% CI, 88.7&ndash, 89.4%). K. pneumoniae isolates harbouring carbapenem-resistance determinants were differentiated from susceptible K. pneumoniae strains with an accuracy of 85% (95% CI, 84.2&ndash, 86.1%). To our knowledge, this is the largest proof of concept demonstration for the utility and feasibility of NIRS to rapidly differentiate between K. pneumoniae and E. coli as well as carbapenem-resistant K. pneumoniae from susceptible strains.

We report the first use of near-infrared spectroscopy for rapid, low-cost, noninvasive detection of Zika virus in Aedes aegypti., The accelerating global spread of arboviruses, such as Zika virus (ZIKV), highlights the need for more proactive mosquito surveillance. However, a major challenge during arbovirus outbreaks has been the lack of rapid and affordable tests for pathogen detection in mosquitoes. We show for the first time that near-infrared spectroscopy (NIRS) is a rapid, reagent-free, and cost-effective tool that can be used to noninvasively detect ZIKV in heads and thoraces of intact Aedes aegypti mosquitoes with prediction accuracies of 94.2 to 99.3% relative to quantitative reverse transcription polymerase chain reaction (RT-qPCR). NIRS involves simply shining a beam of light on a mosquito to collect a diagnostic spectrum. We estimated in this study that NIRS is 18 times faster and 110 times cheaper than RT-qPCR. We anticipate that NIRS will be expanded upon for identifying potential arbovirus hotspots to guide the spatial prioritization of vector control.

Background:\ud \ud Transfluthrin vapour prevents mosquito bites by disrupting their host-seeking behaviors. We measured the additional benefits of combining transfluthrin-treated sisal decorations and long-lasting insecticidal nets (LLINs) with an aim of extending protection against early evening, indoor-biting malaria vectors when LLINs are ineffective.\ud Methods:\ud \ud We investigated the indoor protective efficacy of locally made sisal decorative baskets (0.28 m2) treated with 2.5 ml and 5.0 ml transfluthrin, in terms of mosquito density, exposure to bites and 24 h mortality. Experiments were conducted in experimental huts, located in Lupiro village, Ulanga District, south-eastern Tanzania. Human landing catches (HLC) were used to measure exposure to bites between 19:00–23:00 h. Each morning, at 06:00 h, mosquitoes were collected inside huts and in exit traps and monitored for 24 h mortality.\ud Results:\ud \ud Sisal decorative baskets (0.28 m2) treated with 2.5 ml and 5.0 ml transfluthrin deterred three-quarters of Anopheles arabiensis mosquitoes from entering huts (relative rate, RR = 0.26, 95% confidence interval, CI: 0.20–0.34, P < 0.001 and RR= 0.29, 95% CI: 0.22–0.37, P < 0.001, respectively). Both treatments induced a 10-fold increase in 24 h mortality of An. arabiensis mosquitoes (odds ratio, OR = 12.26, 95% CI: 7.70–19.51, P < 0.001 and OR = 18.42, 95% CI: 11.36–29.90, P < 0.001, respectively).\ud Conclusions:\ud \ud Sisal decorative items treated with spatial repellents provide additional household and personal protection against indoor biting malaria and nuisance mosquitoes in the early evening, when conventional indoor vector control tools, such as LLINs, are not in use. We recommend future studies to investigate the epidemiological relevance of combining LLINs and transfluthrin decorated baskets in terms of their effect on reduction in malaria prevalence.

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.

Background: A number of mosquito vectors bite and rest outdoors, which contributes to sustained residual\ud malaria transmission in endemic areas. Spatial repellents are thought to create a protective “bubble” within which\ud mosquito bites are reduced and may be ideal for outdoor use. This study builds on previous studies that proved\ud efficacy of transfluthrin-treated hessian strips against outdoor biting mosquitoes. The goal of this study was to\ud modify strips into practical, attractive and acceptable transfluthrin treated sisal and hessian emanators that confer\ud protection against potential infectious bites before people use bed nets especially in the early evening and\ud outdoors. This study was conducted in Kilombero Valley, Ulanga District, south-eastern Tanzania.\ud Results: The protective efficacy of hand-crafted transfluthrin-treated sisal decorative baskets and hessian wall\ud decorations against early evening outdoor biting malaria vectors was measured by human landing catches (HLC) in\ud outdoor bars during peak outdoor mosquito biting activity (19:00 to 23:00 h). Treated baskets and wall decorations\ud reduced bites of Anopheles arabiensis mosquitoes by 89% (Relative Rate, RR = 0.11, 95% confidence interval, CI: 0.09–\ud 0.15, P < 0.001) and 86% (RR = 0.14, 95% CI: 0.11–0.18, P < 0.001), respectively. In addition, they significantly reduced\ud exposure to outdoor bites of Culex spp. by 66% (RR = 0.34, 95% CI: 0.22–0.52, P < 0.001) and 56% (RR = 0.44, 95% CI:\ud 0.29–0.66, P < 0.001), respectively.\ud Conclusion: Locally hand-crafted transfluthrin-treated sisal decorative baskets and hessian wall decorations are\ud readily acceptable and confer protection against outdoor biting malaria vectors in the early evening and outdoors:\ud when people are resting on the verandas, porches or in outdoor social places such as bars and restaurants.\ud Additional research can help support the use of such items as complementary interventions to expand protection\ud to communities currently experiencing outdoor transmission of mosquito-borne pathogens.

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., Author Summary 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.

Background Near infrared spectroscopy (NIRS) is a high throughput technique that measures absorbance of specific wavelengths of light by biological samples and uses this information to classify the age of lab-reared mosquitoes as younger or older than seven days with an average accuracy greater than 80%. For NIRS to estimate ages of wild mosquitoes, a sample of wild mosquitoes with known age in days would be required to train and test the model. Mark-release-recapture is the most reliable method to produce wild-caught mosquitoes of known age in days. However, it is logistically demanding, time inefficient, subject to low recapture rates, and raises ethical issues due to the release of mosquitoes. Using labels from Detinova dissection results in a mathematical model with poor accuracy. Alternatively, a model trained on spectra from laboratory-reared mosquitoes where age in days is known can be applied to estimate the age of wild mosquitoes, but this would be appropriate only if spectra collected from laboratory-reared and wild mosquitoes are similar. Methods and findings We performed k-means (k = 2) cluster analysis on a mixture of spectra collected from lab-reared and wild Anopheles arabiensis to determine if there is any significant difference between these two groups. While controlling the numbers of mosquitoes included in the model at each age, we found two clusters with no significant difference in distribution of spectra collected from lab-reared and wild mosquitoes (p = 0.25). We repeated the analysis using hierarchical clustering, and similarly, no significant difference was observed (p = 0.13). Conclusion We find no difference between spectra collected from laboratory-reared and wild mosquitoes of the same age and species. The results strengthen and support the on-going practice of applying the model trained on spectra collected from laboratory-reared mosquitoes, especially first-generation laboratory-reared mosquitoes.

Background Understanding biting distribution of potentially infectious (parous) mosquitoes at various hours of the night would be useful in establishing the likely impact of bed nets on malaria transmission. Bed nets are highly effective at preventing biting by older malaria vectors, which occurs when most people are in bed. However, this behaviour is likely to vary across ecological settings and among mosquito populations. Methods Field experiments were conducted in Minepa village within Kilombero Valley. Two outdoor catching stations located approximately 50 m from each other were established for mosquito collection. On each experimental night, mosquitoes were collected using human landing catch (HLC) by a single adult male at each station from 18:00 to 07:00 h. To compare the distribution of mosquito biting and the composition of their age structure, mosquitoes were sorted and recorded according to the hour they were collected. A sub-sample of Anopheles arabiensis was dissected to determine their parity status. Insectary-reared An. arabiensis within the semi-field system (SFS) with known age were also released in the SFS (10 m × 20 m) and recaptured hourly using HLC to determine the effect of parity on biting distribution. Results Overall, there was no statistical association between the parity status and the biting time of An. arabiensis either in the field or in the SFS (P ≥ 0.05). The wild and insectary-reared An. arabiensis were observed to exhibit different hourly biting patterns. Conclusion The study has shown that mosquito biting time phenotype is not influenced by their parity status. These findings imply that the risk of human exposure to potentially infectious bites is equally distributed throughout the night, thus supplementary measures to protect people against bites in evening and morning are desirable.