Using mobile phones as acoustic sensors for high-throughput mosquito surveillance

Smart technology for mosquito control: Recent developments, challenges, and future prospects

Smart technology coupled with digital sensors and deep learning networks have emerging scopes in various fields, including surveillance of mosquitoes. Several studies have been conducted to examine the efficacy of such technologies in the differential identification of mosquitoes with high accuracy. Some smart trap uses computer vision technology and deep learning networks to identify live Aedes aegypti and Culex quinquefasciatus in real time. Implementing such tools integrated with a reliable capture mechanism can be beneficial in identifying live mosquitoes without destroying their morphological features. Such smart traps can correctly differentiates between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak. Smart devices embedded with YOLO V4 Deep Neural Network algorithm has been designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The use of acoustic and optical sensors in combination with machine learning techniques have escalated the automatic classification of mosquitoes based on their flight characteristics, including wing-beat frequency. Thus, such Artificial Intelligence-based tools have promising scopes for surveillance of mosquitoes to control vector-borne diseases. However working efficiency of such technologies requires further evaluation for implementation on a global scale.

Differences in male Aedes aegypti and Aedes albopictus hearing systems facilitate recognition of conspecific female flight tones

When Aedes albopictus mosquitoes invade regions predominated by Aedes aegypti, either the latter can be displaced or the species can coexist, with potential consequences on disease transmission. Males from both species identify females by listening for her flight sounds. Comparing male hearing systems may provide insight into how hearing could prevent interspecific mating. Here, we show that species-specific differences in female wing beat frequencies are reflected in differences in male ear mechanical tuning frequencies and sound response profiles. Though Aedes albopictus males are attracted to sound, they do not readily display abdominal bending, unlike Aedes aegypti. We observed interspecific differences in male ear mechanical, but not electrical, tuning, suggesting a conserved primary auditory processing pathway. Our work suggests a potential role for hearing in the premating isolation of Aedes aegypti and Aedes albopictus, with implications for predicting future dynamics in their sympatric relationships and our understanding of mosquito acoustic communication.

Surveillance of mosquitoes harnessing their buzzing sound

Mosquito surveillance for vector-borne disease management relies on traditional morphological and molecular techniques, which are tedious, time-consuming, and costly. The present study describes a simple and efficient recording device that analyzes mosquito sound to estimate species composition, male-female ratio, fed-unfed status, and harmonic convergence interaction using fundamental frequency (F0) bandwidth, harmonics, amplitude, and combinations of these parameters. The study examined a total of 19 mosquito species, including 3 species of Aedes, 7 species of Anopheles, 1 species of Armigeres, 5 species of Culex, 1 species of Hulecoetomyia, and 2 species of Mansonia. Among them, the F0 ranges between 269.09 ± 2.96 Hz (Anopheles culiciformis) to 567.51 ± 3.82 Hz (Aedes vittatus) and the harmonic band (hb) number ranges from 5 (An. culiciformis) to 12 (Ae. albopictus). In terms of species identification, the success rate was 95.32 % with F0, 84.79 % with F0-bandwidth, 84.79 % with harmonic band (hb) diversity, and 49.7 % with amplitude (dB). The species identification rate has gone up to 96.50 % and 97.66 % with the ratio and multiplication of F0 and hb, respectively. This is because of the matrices that combine multiple sound attributes. Comparatively, combinations of the amplitude of the F0 and the higher harmonic frequency band were non-significant for species identification (60.82 %). The fed females have shown a considerable increase in F0 in comparison to the unfed. The males of all the species possessed significantly higher frequencies with respect to the females. Interestingly, the presence of male-female of Ae. vittatus together showed harmonic convergence between the 2nd and 3rd harmonic bands. In conclusion, the sound-based technology is simple, precise, and cost-effective and provides better resolution for species, sex, and fed-unfed status detection in comparison to conventional methods. Real-time surveillance of mosquitoes could potentially utilize this technology.

Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex

BACKGROUND

Mosquito-borne diseases are a major concern for public and veterinary health authorities, highlighting the importance of effective vector surveillance and control programs. Traditional surveillance methods are labor-intensive and do not provide high temporal resolution, which may hinder a full assessment of the risk of mosquito-borne pathogen transmission. Emerging technologies for automated remote mosquito monitoring have the potential to address these limitations; however, few studies have tested the performance of such systems in the field.

METHODS

In the present work, an optical sensor coupled to the entrance of a standard mosquito suction trap was used to record 14,067 mosquito flights of Aedes and Culex genera at four temperature regimes in the laboratory, and the resulting dataset was used to train a machine learning (ML) model. The trap, sensor, and ML model, which form the core of an automated mosquito surveillance system, were tested in the field for two classification purposes: to discriminate Aedes and Culex mosquitoes from other insects that enter the trap and to classify the target mosquitoes by genus and sex. The field performance of the system was assessed using balanced accuracy and regression metrics by comparing the classifications made by the system with those made by the manual inspection of the trap.

RESULTS

The field system discriminated the target mosquitoes (Aedes and Culex genera) with a balanced accuracy of 95.5% and classified the genus and sex of those mosquitoes with a balanced accuracy of 88.8%. An analysis of the daily and seasonal temporal dynamics of Aedes and Culex mosquito populations was also performed using the time-stamped classifications from the system.

CONCLUSIONS

This study reports results for automated mosquito genus and sex classification using an optical sensor coupled to a mosquito trap in the field with highly balanced accuracy. The compatibility of the sensor with commercial mosquito traps enables the sensor to be integrated into conventional mosquito surveillance methods to provide accurate automatic monitoring with high temporal resolution of Aedes and Culex mosquitoes, two of the most concerning genera in terms of arbovirus transmission.

Automated differentiation of mixed populations of free-flying female mosquitoes under semi-field conditions

Great advances in automated identification systems, or 'smart traps', that differentiate insect species have been made in recent years, yet demonstrations of field-ready devices under free-flight conditions remain rare. Here, we describe the results of mixed-species identification of female mosquitoes using an advanced optoacoustic smart trap design under free-flying conditions. Point-of-capture classification was assessed using mixed populations of congeneric (Aedes albopictus and Aedes aegypti) and non-congeneric (Ae. aegypti and Anopheles stephensi) container-inhabiting species of medical importance. Culex quinquefasciatus, also common in container habitats, was included as a third species in all assessments. At the aggregate level, mixed collections of non-congeneric species (Ae. aegypti, Cx. quinquefasciatus, and An. stephensi) could be classified at accuracies exceeding 90% (% error = 3.7-7.1%). Conversely, error rates increased when analysing individual replicates (mean % error = 48.6; 95% CI 8.1-68.6) representative of daily trap captures and at the aggregate level when Ae. albopictus was released in the presence of Ae. aegypti and Cx. quinquefasciatus (% error = 7.8-31.2%). These findings highlight the many challenges yet to be overcome but also the potential operational utility of optoacoustic surveillance in low diversity settings typical of urban environments.

Tiny spies: mosquito antennae are sensitive sensors for eavesdropping on frog calls

Most mosquito and midge species use hearing during acoustic mating behaviors. For frog-biting species, however, hearing plays an important role beyond mating as females rely on anuran calls to obtain blood meals. Despite the extensive work examining hearing in mosquito species that use sound in mating contexts, our understanding of how mosquitoes hear frog calls is limited. Here, we directly investigated the mechanisms underlying detection of frog calls by a mosquito species specialized on eavesdropping on anuran mating signals: Uranotaenia lowii. Behavioral, biomechanical and neurophysiological analyses revealed that the antenna of this frog-biting species can detect frog calls by relying on neural and mechanical responses comparable to those of non-frog-biting species. Our findings show that in Ur. lowii, contrary to most species, males do not use sound for mating, but females use hearing to locate their anuran host. We also show that the response of the antennae of this frog-biting species resembles that of the antenna of species that use hearing for mating. Finally, we discuss our data considering how mosquitoes may have evolved the ability to tap into the communication system of frogs.

Tracking technologies: advances driving new insights into monarch migration

Understanding the rules of how monarch butterflies complete their annual North American migration will be clarified by studying them within a movement ecology framework. Insect movement ecology is growing at a rapid pace due to the development of novel monitoring systems that allow ever-smaller animals to be tracked at higher spatiotemporal resolution for longer periods of time. New innovations in tracking hardware and associated software, including miniaturization, energy autonomy, data management, and wireless communication, are reducing the size and increasing the capability of next-generation tracking technologies, bringing the goal of tracking monarchs over their entire migration closer within reach. These tools are beginning to be leveraged to provide insight into different aspects of monarch biology and ecology, and to contribute to a growing capacity to understand insect movement ecology more broadly and its impact on human life.

Effect of age on wingbeat frequency of Aedes aegypti and potential application for age estimation of mosquitoes

To combat mosquito-borne diseases, a variety of vector control tools have been implemented. Estimating age structure in populations of vector species is important for understanding transmission potential. Age-grading techniques have been used as critical methods for evaluating the efficacy of vector control tools. However, methods like mark-release-recapture and ovarian dissection are laborious and require a high level of training. For decades, scientists have discussed the wide array of acoustic signatures of different mosquito species. These distinguishable wingbeat signatures with spatiotemporal classification allow mosquitoes of the same species to locate one another for mating. In recent years, the use of sensitive acoustic devices like mobile phones have proved effective. Wingbeat signatures can be used to identify mosquito species without the challenge of intensive field collections and morphological and molecular identifications. In this study, laboratory Aedes aegypti (L.) female and male wingbeats were recorded using mobile phones to determine whether sex and age differences with chronological time, and across different physiological stages, can be detected. Our results indicate significantly different wingbeat signatures between male and female Ae. aegypti, and a change of wingbeat frequencies with age and reproduction stage in females.

Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system

Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed-trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.

What incentives encourage local communities to collect and upload mosquito sound data by using smartphones? A mixed methods study in Tanzania

BACKGROUND

To detect and identify mosquitoes using their characteristic high-pitched sound, we have developed a smartphone application, known as the 'HumBug sensor', that records the acoustic signature of this sound, along with the time and location. This data is then sent remotely to a server where algorithms identify the species according to their distinctive acoustic signature. Whilst this system works well, a key question that remains is what mechanisms will lead to effective uptake and use of this mosquito survey tool? We addressed this question by working with local communities in rural Tanzania and providing three alternative incentives: money only, short message service (SMS) reminders and money, and SMS reminders only. We also had a control group with no incentive.

METHODS

A multi-site, quantitative empirical study was conducted in four villages in Tanzania from April to August 2021. Consenting participants (n = 148) were recruited and placed into one of the three intervention arms: monetary incentives only; SMS reminders with monetary incentives; and SMS reminders only. There was also a control group (no intervention). To test effectiveness of the mechanisms, the number of audio uploads to the server of the four trial groups on their specific dates were compared. Qualitative focus group discussions and feedback surveys were also conducted to explore participants' perspectives on their participation in the study and to capture their experiences of using the HumBug sensor.

RESULTS

Qualitative data analysis revealed that for many participants (37 out of 81), the main motivation expressed was to learn more about the types of mosquitoes present in their houses. Results from the quantitative empirical study indicate that the participants in the 'control' group switched on their HumBug sensors more over the 14-week period (8 out of 14 weeks) when compared to those belonging to the 'SMS reminders and monetary incentives' trial group. These findings are statistically significant (p < 0.05 or p > 0.95 under a two-sided z-test), revealing that the provision of monetary incentives and sending SMS reminders did not appear to encourage greater number of audio uploads when compared to the control.

CONCLUSIONS

Knowledge on the presence of harmful mosquitoes was the strongest motive for local communities to collect and upload mosquito sound data via the HumBug sensor in rural Tanzania. This finding suggests that most efforts should be made to improve flow of real-time information back to the communities on types and risks associated with mosquitoes present in their houses.

Characterizing the Vector Data Ecosystem

A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct control and mitigation efforts. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, means that it is distributed across many formats and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data - the Vector Data Ecosystem - can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is 'out there', and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data).

Automated detection of the yellow-legged hornet (Vespa velutina) using an optical sensor with machine learning

BACKGROUND

The yellow-legged hornet (Vespa velutina) is native to Southeast Asia and is an invasive alien species of concern in many countries. More effective management of populations of V. velutina could be achieved through more widespread and intensive monitoring in the field, however current methods are labor intensive and costly. To address this issue, we have assessed the performance of an optical sensor combined with a machine learning model to classify V. velutina and native wasps/hornets and bees. Our aim is to use the results of the present work as a step towards the development of a monitoring solution for V. velutina in the field.

RESULTS

We recorded a total 935 flights from three bee species: Apis mellifera, Bombus terrestris and Osmia bicornis; and four wasp/hornet species: Polistes dominula, Vespula germanica, Vespa crabro and V. velutina. The machine learning model achieved an average accuracy for species classification of 80.1 ± 13.9% and 74.5 ± 7.0% for V. velutina. V. crabro had the highest level of misclassification, confused mainly with V. velutina and P. dominula. These results were obtained using a 14-value peak and valley feature derived from the wingbeat power spectral density.

CONCLUSION

This study demonstrates that the wingbeat recordings from a flying insect sensor can be used with machine learning methods to differentiate V. velutina from six other Hymenoptera species in the laboratory and this knowledge could be used to help develop a tool for use in integrated invasive alien species management programs. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Applications and advances in acoustic monitoring for infectious disease epidemiology

Emerging infectious diseases continue to pose a significant burden on global public health, and there is a critical need to better understand transmission dynamics arising at the interface of human activity and wildlife habitats. Passive acoustic monitoring (PAM), more typically applied to questions of biodiversity and conservation, provides an opportunity to collect and analyse audio data in relative real time and at low cost. Acoustic methods are increasingly accessible, with the expansion of cloud-based computing, low-cost hardware, and machine learning approaches. Paired with purposeful experimental design, acoustic data can complement existing surveillance methods and provide a novel toolkit to investigate the key biological parameters and ecological interactions that underpin infectious disease epidemiology.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape Activities

The rapid expansion in miniaturization, usability, energy efficiency, and affordability of Internet of Things (IoT) sensors, integrated with innovations in smart capability, is greatly increasing opportunities in ground-level monitoring of ecosystems at a specific scale using sensor grids. Surrounding sound is a powerful data source for investigating urban and non-urban ecosystem health, and researchers commonly use robust but expensive passive sensors as monitoring equipment to capture it. This paper comprehensively describes the hardware behind our low-cost, small multipurpose prototype, capable of monitoring different environments (e.g., remote locations) with onboard processing power. The device consists of a printed circuit board, microprocessor, local memory, environmental sensor, microphones, optical sensors and LoRa (Long Range) communication systems. The device was successfully used in different use cases, from monitoring mosquitoes enhanced with optical sensors to ocean activities using a hydrophone.

Chasing Flies: The Use of Wingbeat Frequency as a Communication Cue in Calyptrate Flies (Diptera: Calyptratae)

The incidental sound produced by the oscillation of insect wings during flight provides an opportunity for species identification. Calyptrate flies include some of the fastest and most agile flying insects, capable of rapid changes in direction and the fast pursuit of conspecifics. This flight pattern makes the continuous and close recording of their wingbeat frequency difficult and limited to confined specimens. Advances in sound editor and analysis software, however, have made it possible to isolate low amplitude sounds using noise reduction and pitch detection algorithms. To explore differences in wingbeat frequency between genera and sex, 40 specimens of three-day old Sarcophaga crassipalpis, Lucilia sericata, Calliphora dubia, and Musca vetustissima were individually recorded in free flight in a temperature-controlled room. Results showed significant differences in wingbeat frequency between the four species and intersexual differences for each species. Discriminant analysis classifying the three carrion flies resulted in 77.5% classified correctly overall, with the correct classification of 82.5% of S. crassipalpis, 60% of C. dubia, and 90% of L. sericata, when both mean wingbeat frequency and sex were included. Intersexual differences were further demonstrated by male flies showing significantly higher variability than females in three of the species. These observed intergeneric and intersexual differences in wingbeat frequency start the discussion on the use of the metric as a communication signal by this taxon. The success of the methodology demonstrated differences at the genus level and encourages the recording of additional species and the use of wingbeat frequency as an identification tool for these flies.

Building International Capacity for Citizen Scientist Engagement in Mosquito Surveillance and Mitigation: The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper

Simple Summary

The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper is a free citizen science data collection tool that can be downloaded onto smartphones. The Mosquito Habitat Mapper encourages individuals to participate in locating and removing mosquito breeding habitats from use. An easy-to-use graphic interface enables users to report and describe mosquito breeding habitats, places with standing water where immature mosquitoes grow and develop. Citizen scientists are asked to determine if they see immature mosquitoes and if they wish to count and identify any mosquito larvae they see. In the last task, the user is asked to dump out or cover the standing water source, eliminating its use as a breeding habitat. In this way, the GLOBE Observer mobile app also supports the actions of individuals protecting their communities from mosquito-borne disease. In addition, all data reported by citizen scientists are publicly available. Scientists are accessing this data for a variety of research uses, including the development of automated techniques to recognize larvae and mosquito breeding sites from digital images. Since 2017, more than 32,000 Mosquito Habitat Mapper observations have been submitted by citizen scientists in 84 countries.

Abstract

The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper is a no-cost citizen scientist data collection tool compatible with Android and iOS devices. Available in 14 languages and 126 countries, it supports mosquito vector surveillance, mitigation, and education by interested individuals and as part of participatory community surveillance programs. For low-resource communities where mosquito control services are inadequate, the Mosquito Habitat Mapper supports local health action, empowerment, and environmental justice. The tangible benefits to human health supported by the Mosquito Habitat Mapper have encouraged its wide adoption, with more than 32,000 observations submitted from 84 countries. The Mosquito Habitat Mapper surveillance and data collection tool is complemented by an open database, a map visualization interface, data processing and analysis tools, and a supporting education and outreach campaign. The mobile app tool and associated research and education assets can be rapidly deployed in the event of a pandemic or local disease outbreak, contributing to global readiness and resilience in the face of mosquito-borne disease. Here, we describe the app, the Mosquito Habitat Mapper information system, examples of Mosquito Habitat Mapper deployment in scientific research, and the outreach campaign that supports volunteer training and STEM education of students worldwide.

A ResNet attention model for classifying mosquitoes from wing-beating sounds

Mosquitoes are vectors of numerous deadly diseases, and mosquito classification task is vital for their control programs. To ease manual labor and time-consuming classification tasks, numerous image-based machine-learning (ML) models have been developed to classify different mosquito species. Mosquito wing-beating sounds can serve as a unique classifier for mosquito classification tasks, which can be adopted easily in field applications. The current study aims to develop a deep neural network model to identify six mosquito species of three different genera, based on their wing-beating sounds. While existing models focused on raw audios, we developed a comprehensive pre-processing step to convert raw audios into more informative Mel-spectrograms, resulting in more robust and noise-free extracted features. Our model, namely 'Wing-beating Network' or 'WbNet', combines the state-of-art residual neural network (ResNet) model as a baseline, with self-attention mechanism and data-augmentation technique, and outperformed other existing models. The WbNet achieved the highest performance of 89.9% and 98.9% for WINGBEATS and ABUZZ data respectively. For species of Aedes and Culex genera, our model achieved 100% precision, recall and F1-scores, whereas, for Anopheles, the WbNet reached above 95%. We also compared two existing wing-beating datasets, namely WINGBEATS and ABUZZ, and found our model does not need sophisticated audio devices, hence performed better on ABUZZ audios, captured on usual mobile devices. Overall, our model has potential to serve in mosquito monitoring and prevalence studies in mosquito eradication programs, along with potential implementation in classification tasks of insect pests or other sound-based classifications.

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

Background

Every year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosquitoes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxonomical identification. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classification of mosquitoes based on their flight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the field, which could lead to significant improvements in vector surveillance.

Methods

A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classification of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of five different machine learning algorithms to achieve the best model for mosquito classification.

Results

The best accuracy results achieved using machine learning were: 94.2% for genus classification, 99.4% for sex classification of Aedes, and 100% for sex classification of Culex. The best algorithms and features were deep neural network with spectrogram for genus classification and gradient boosting with Mel Frequency Cepstrum Coefficients among others for sex classification of either genus.

Conclusions

To our knowledge, this is the first time that a sensor coupled to a standard mosquito suction trap has provided automatic classification of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05324-5.

A handmade trap for malaria mosquito surveillance by citizens in Rwanda

For effective sampling of mosquitoes in malaria surveillance programmes, it is essential to include attractive cues in traps. With the aim of implementing a citizen science project on malaria vectors in rural Rwanda, a handmade plastic bottle trap was designed and tested in the field to determine its effectiveness in capturing adult Anopheles gambiae sensu lato, the main malaria vector, and other mosquito species. Carbon dioxide (CO2) and light were used as attractive cues. CO2 was produced by inoculating sugar with yeast and water. Light was emitted from a torch by light-emitting diodes (LEDs). Under field conditions in rural Rwanda, three handmade trap designs were compared to Centers for Disease Control and Prevention miniature light traps (CDC-LT) in houses. The trap baited with yeast produced CO2 and light caught the highest number of mosquitoes compared to the traps baited with light alone or CO2 alone. The number of An. gambiae s.l. in the handmade trap with light and CO2 was approximately 9–10% of the number caught with a CDC light trap. This suggests that about 10 volunteers with a handmade trap could capture a similar-sized sample of An. gambiae as one CDC-LT would collect. Based on these findings, the handmade plastic bottle trap baited with sugar fermenting yeast and light represents an option for inclusion in mosquito surveillance activities in a citizen science context.

Automating insect monitoring using unsupervised near-infrared sensors

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor’s capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman’s rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.

Harmonic convergence coordinates swarm mating by enhancing mate detection in the malaria mosquito Anopheles gambiae

The mosquito Anopheles gambiae is a major African malaria vector, transmitting parasites responsible for significant mortality and disease burden. Although flight acoustics are essential to mosquito mating and present promising alternatives to insecticide-based vector control strategies, there is limited data on mosquito flight tones during swarming. Here, for the first time, we present detailed analyses of free-flying male and female An. gambiae flight tones and their harmonization (harmonic convergence) over a complete swarm sequence. Audio analysis of single-sex swarms showed synchronized elevation of male and female flight tones during swarming. Analysis of mixed-sex swarms revealed additional 50 Hz increases in male and female flight tones due to mating activity. Furthermore, harmonic differences between male and female swarm tones in mixed-sex swarms and in single-sex male swarms with artificial female swarm audio playback indicate that frequency differences of approximately 50 Hz or less at the male second and female third harmonics (M2:F3) are maintained both before and during mating interactions. This harmonization likely coordinates male scramble competition by maintaining ideal acoustic recognition within mating pairs while acoustically masking phonotactic responses of nearby swarming males to mating females. These findings advance our knowledge of mosquito swarm acoustics and provide vital information for reproductive control strategies.

Resolving the identification of weak-flying insects during flight: a coupling between rigorous data processing and biology

Bioacoustic methods play an increasingly important role for the detection of insects in a range of surveillance and monitoring programmes.Weak-flying insects evade detection because they do not yield sufficient audio information to capture wingbeat and harmonic frequencies. These inaudible insects often pose a significant threat to food security as pests of key agricultural crops worldwide.Automatic detection of such insects is crucial to the future of crop protection by providing critical information to assess the risk to a crop and the need for preventative measures.We describe an experimental set-up designed to derive audio recordings from a range of weak-flying aphids and beetles using an LED array.A rigorous data processing pipeline was developed to extract meaningful features, linked to morphological characteristics, from the audio and harmonic series for six aphid and two beetle species.An ensemble of over 50 bioacoustic parameters was used to achieve species discrimination with a success rate of 80%. The inclusion of the dominant and fundamental frequencies improved prediction between beetles and aphids because of large differences in wingbeat frequencies.At the species level, error rates were minimized when harmonic features were supplemented by features indicative of differences in species' flight energies.

Toys or Tools? Utilization of Unmanned Aerial Systems in Mosquito and Vector Control Programs

Organized mosquito control programs (MCP) in the United States have been protecting public health since the early 1900s. These programs utilize integrated mosquito management for surveillance and control measures to enhance quality of life and protect the public from mosquito-borne diseases. Because much of the equipment and insecticides are developed for agriculture, MCP are left to innovate and adapt what is available to accomplish their core missions. Unmanned aerial systems (UAS) are one such innovation that are quickly being adopted by MCP. The advantages of UAS are no longer conjectural. In addition to locating mosquito larval habitats, UAS affords MCP real-time imagery, improved accuracy of aerial insecticide applications, mosquito larval detection and sampling. UAS are also leveraged for applying larvicides to water in habitats that range in size from multi-acre wetlands to small containers in urban settings. Employing UAS can reduce staff exposure to hazards and the impact associated with the use of heavy equipment in sensitive habitats. UAS are utilized by MCP nationally and their use will continue to increase as technology advances and regulations change. Current impediments include a dearth of major UAS manufacturers of equipment that is tailor-made for mosquito control, pesticides that are optimized for application via UAS and regulations that limit the access of UAS to national airspace. This manuscript highlights the strengths and weaknesses of UAS within MCP, provides an update on systems and methods used, and charts the future direction of UAS technology within MCP tasked with public health protection.

Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study

INTRODUCTION

Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms.

METHODS AND ANALYSIS

Participants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the ethics committee of the Centre Hospitalier de l'Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences.

TRIAL REGISTRATION NUMBER

NCT04762693.

TriatoDex, an electronic identification key to the Triatominae (Hemiptera: Reduviidae), vectors of Chagas disease: Development, description, and performance

Correct identification of triatomine bugs is crucial for Chagas disease surveillance, yet available taxonomic keys are outdated, incomplete, or both. Here we present TriatoDex, an Android app-based pictorial, annotated, polytomous key to the Triatominae. TriatoDex was developed using Android Studio and tested by 27 Brazilian users. Each user received a box with pinned, number-labeled, adult triatomines (33 species in total) and was asked to identify each bug to the species level. We used generalized linear mixed models (with user- and species-ID random effects) and information-theoretic model evaluation/averaging to investigate TriatoDex performance. TriatoDex encompasses 79 questions and 554 images of the 150 triatomine-bug species described worldwide up to 2017. TriatoDex-based identification was correct in 78.9% of 824 tasks. TriatoDex performed better in the hands of trained taxonomists (93.3% vs. 72.7% correct identifications; model-averaged, adjusted odds ratio 5.96, 95% confidence interval [CI] 3.09–11.48). In contrast, user age, gender, primary job (including academic research/teaching or disease surveillance), workplace (including universities, a reference laboratory for triatomine-bug taxonomy, or disease-surveillance units), and basic training (from high school to biology) all had negligible effects on TriatoDex performance. Our analyses also suggest that, as TriatoDex results accrue to cover more taxa, they may help pinpoint triatomine-bug species that are consistently harder (than average) to identify. In a pilot comparison with a standard, printed key (370 tasks by seven users), TriatoDex performed similarly (84.5% correct assignments, CI 68.9–94.0%), but identification was 32.8% (CI 24.7–40.1%) faster on average–for a mean absolute saving of ~2.3 minutes per bug-identification task. TriatoDex holds much promise as a handy, flexible, and reliable tool for triatomine-bug identification; an updated iOS/Android version is under development. We expect that, with continuous refinement derived from evolving knowledge and user feedback, TriatoDex will substantially help strengthen both entomological surveillance and research on Chagas disease vectors.

Citizen science as a tool for arboviral vector surveillance in a resourced-constrained setting: results of a pilot study in Honiara, Solomon Islands, 2019

Background

Recent arboviral disease outbreaks highlight the value a better understanding of the spread of disease-carrying mosquitoes across spatial-temporal scales can provide. Traditional surveillance tools are limited by jurisdictional boundaries, workforce constraints, logistics, and cost; factors that in low- and middle-income countries often conspire to undermine public health protection efforts. To overcome these, we undertake a pilot study designed to explore if citizen science provides a feasible strategy for arboviral vector surveillance in small developing Pacific island contexts.

Methods

We recruited, trained, and equipped community volunteers to trap and type mosquitos within their household settings, and to report count data to a central authority by short-message-service. Mosquito catches were independently assessed to measure participants’ mosquito identification accuracy. Other data were collected to measure the frequency and stability of reporting, and volunteers’ experiences.

Results

Participants collected data for 78.3% of the study period, and agreement between the volunteer citizen scientists’ and the reviewing entomologist’s mosquito identification was 94%. Opportunity to contribute to a project of social benefit, the chance to learn new skills, and the frequency of engagement with project staff were prime motivators for participation. Unstable electricity supply (required to run the trap’s fan), insufficient personal finances (to buy electricity and phone credit), and inconvenience were identified as barriers to sustained participation.

Conclusions

While there are challenges to address, our findings suggest that citizen science offers an opportunity to overcome the human resource constraints that conspire to limit health authorities’ capacity to monitor arboviral vectors across populations. We note that the success of citizen science-based surveillance is dependent on the appropriate selection of equipment and participants, and the quality of engagement and support provided.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10493-6.

Flight tone characterisation of the South American malaria vector Anopheles darlingi (Diptera: Culicidae)

BACKGROUND

Flight tones play important roles in mosquito reproduction. Several mosquito species utilise flight tones for mate localisation and attraction. Typically, the female wingbeat frequency (WBF) is lower than males, and stereotypic acoustic behaviors are instrumental for successful copulation. Mosquito WBFs are usually an important species characteristic, with female flight tones used as male attractants in surveillance traps for species identification. Anopheles darlingi is an important Latin American malaria vector, but we know little about its mating behaviors.

OBJECTIVES

We characterised An. darlingi WBFs and examined male acoustic responses to immobilised females.

METHODS

Tethered and free flying male and female An. darlingi were recorded individually to determine their WBF distributions. Male-female acoustic interactions were analysed using tethered females and free flying males.

FINDINGS

Contrary to most mosquito species, An. darlingi females are smaller than males. However, the male's WBF is ~1.5 times higher than the females, a common ratio in species with larger females. When in proximity to a female, males displayed rapid frequency modulations that decreased upon genitalia engagement. Tethered females also modulated their frequency upon male approach, being distinct if the interaction ended in copulation or only contact.

MAIN CONCLUSIONS

This is the first report of An. darlingi flight acoustics, showing that its precopulatory acoustics are similar to other mosquitoes despite the uncommon male:female size ratio, suggesting that WBF ratios are common communication strategies rather than a physical constraint imposed by size.

The Effect of Sound Lure Frequency and Habitat Type on Male Aedes albopictus (Diptera: Culicidae) Capture Rates With the Male Aedes Sound Trap

The global distribution of Aedes albopictus (Skuse) is rapidly expanding which has contributed to the emergence and re-emergence of dengue and chikungunya outbreaks. Improvements in vector surveillance are necessary to facilitate optimized, evidence-based vector control operations. Current trapping technology used to target Ae. albopictus and other Aedes species for vector surveillance are limited in both scale and scope, thus novel tools are required. Here, we evaluated the Male Aedes Sound Trap (MAST) for its capacity to sample male Ae. albopictus. Aims of this study were twofold: 1) to determine the most effective frequency for capturing male Ae. albopictus and 2) to investigate fine-scale variations in male Ae. albopictus abundance. MASTs which produced sound lure frequencies between 500 and 650 Hz captured significantly more male Ae. albopictus than those with sound lure frequencies set to 450 Hz. Further, the higher sound lure frequency of 700 Hz significantly reduced catches relative to 650 Hz. MASTs placed in woodland habitats captured significantly more male Ae. albopictus than MASTs placed near houses. These results provide baseline information for optimizing sound lure frequencies and placement of the MAST to sample male Ae. albopictus in remote areas.

Deep learning approaches for challenging species and gender identification of mosquito vectors

Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

The presence of knockdown resistance mutations reduces male mating competitiveness in the major arbovirus vector, Aedes aegypti

Background

The development of insecticide resistance in mosquitoes can have pleiotropic effects on key behaviours such as mating competition and host-location. Documenting these effects is crucial for understanding the dynamics and costs of insecticide resistance and may give researchers an evidence base for promoting vector control programs that aim to restore or conserve insecticide susceptibility.

Methods and findings

We evaluated changes in behaviour in a backcrossed strain of Aedes aegypti, homozygous for two knockdown resistance (kdr) mutations (V1016G and S989P) isolated in an otherwise fully susceptible genetic background. We compared biting activity, host location behaviours, wing beat frequency (WBF) and mating competition between the backcrossed strain, and the fully susceptible and resistant parental strains from which it was derived. The presence of the homozygous kdr mutations did not have significant effects on blood avidity, the time to locate a host, or WBF in females. There was, however, a significant reduction in mean WBF in males and a significant reduction in estimated male mating success (17.3%), associated with the isolated kdr genotype.

Conclusions

Our results demonstrate a cost of insecticide resistance associated with an isolated kdr genotype and manifest as a reduction in male mating success. While there was no recorded difference in WBF between the females of our strains, the significant reduction in male WBF recorded in our backcrossed strain might contribute to mate-recognition and mating disruption. These consequences of resistance evolution, especially when combined with other pleiotropic fitness costs that have been previously described, may encourage reversion to susceptibility in the absence of insecticide selection pressures. This offers justification for the implementation of insecticide resistance management strategies based on the rotation or alternation of different insecticide classes in space and time.

The mosquito Aedes aegypti is the main vector of dengue, chikungunya, and Zika. Its control relies heavily on the use of insecticides but the rapid evolution of resistance to these chemicals compromises their efficacy. The conservation or restoration of insecticide susceptibility in Ae. aegypti populations is therefore of great importance. Insecticide susceptibility can be encouraged if the evolution of resistance is accompanied by fitness costs that favour susceptible mosquitoes in the absence of insecticides. This paper documents the first report of a reduction in mating success directly associated with an isolated mutation that confers insecticide resistance in Ae. aegypti. This change in behaviour appears related to alterations in male wing-beat frequency. Our results provide evidence of behavioural changes related to insecticide resistance in Ae. aegypti, suggesting a competitive advantage of susceptible individuals in the absence of insecticides in the field.

Outcomes from international field trials with Male Aedes Sound Traps: Frequency-dependent effectiveness in capturing target species in relation to bycatch abundance

Aedes aegypti and Aedes albopictus vector dengue, chikungunya and Zika viruses. With both species expanding their global distributions at alarming rates, developing effective surveillance equipment is a continuing priority for public health researchers. Sound traps have been shown, in limited testing, to be highly species-specific when emitting a frequency corresponding to a female mosquito wingbeat. Determining male mosquito capture rates in sound traps based on lure frequencies in endemic settings is the next step for informed deployment of these surveillance tools. We field-evaluated Male Aedes Sound Traps (MASTs) set to either 450 Hz, 500 Hz, 550 Hz or 600 Hz for sampling Aedes aegypti and/or Aedes albopictus and compared catch rates to BG-Sentinel traps within Pacific (Madang, Papua New Guinea) and Latin American (Molas, Mexico and Orange Walk Town, Belize) locations. MASTs set to 450-550 Hz consistently caught male Ae. aegypti at rates comparable to BG-Sentinel traps in all locations. A peak in male Ae. albopictus captures in MASTs set at 550 Hz was observed, with the lowest mean abundance recorded in MASTs set to 450 Hz. While significantly higher abundances of male Culex were sampled in MASTs emitting lower relative frequencies in Molas, overall male Culex were captured in significantly lower abundances in the MASTs, relative to BG-Sentinel traps within all locations. Finally, significant differences in rates at which male Aedes and Culex were positively detected in trap-types per weekly collections were broadly consistent with trends in abundance data per trap-type. MASTs at 550 Hz effectively captured both male Ae. aegypti and Ae. albopictus while greatly reducing bycatch, especially male Culex, in locations where dengue transmission has occurred. This high species-specificity of the MAST not only reduces staff-time required to sort samples, but can also be exploited to develop an accurate smart-trap system-both outcomes potentially reducing public health program expenses.

Acoustotactic response of mosquitoes in untethered flight to incidental sound

Mosquitoes are vectors for some of the most devastating diseases on the planet. Given the centrality of acoustic sensing in the precopulatory behavior of these vectors, the use of an exogenous acoustic stimulus offers the potential of interfering with the courtship behavior of these insects. Previous research on the acoustotactic response of mosquitoes has been conducted on tethered preparations using low-intensity sound stimuli. To quantify differences in acoustotactic responses between mosquitos of distinct sex and species, we examined the effects of incidental sound stimuli on the flight behavior of free-flying male vs. female Aedes aegypti and Anopheles gambiae mosquitoes. The key variables were sound frequency (100–1000 Hz) and intensity (67–103 dB, measured at 12.5 cm from the source), and the acoustotactic response was measured in terms of the relative increase in flight speed in response to the stimulus. The data show, for the first time, significant sex- and species-specific differences in acoustotactic responses. A. aegypti exhibited a greater response to sound stimulus compared to An. gambiae, and the response also extended over a larger range of frequencies. Furthermore, the males of both species displayed a greater acoustotactic response than females, with An. gambiae females exhibiting minimal response to sound.

The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

BACKGROUND

The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes.

METHODS

We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced.

RESULTS

The systematic literature search identified 1289 records, 32 of which met the inclusion criteria and 4 records from the reference lists. A total of 36 studies were included coming from twenty different countries. Five mobile phone services were identified in this review: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). Mobile phones were used for surveillance, prevention, diagnosis, and communication demonstrating good performance, acceptance and usability by users, as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors/ disease and improving users-oriented behaviour changes. It can be concluded that there are benefits for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases. Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

Performance of a Low-Cost Acoustic Insect Detector System with Sitophilus oryzae (Coleoptera: Curculionidae) in Stored Grain and Tribolium castaneum (Coleoptera: Tenebrionidae) in Flour

Reduction of postharvest losses is gaining increased priority in warm regions where insect infestation may cause rapid deterioration of staple commodities. Acoustic detection can be used to assess the likelihood of insect infestations in bags of grain, flour, and other commodities that are stored in small holdings in developing countries, enabling rapid targeting of treatments. A portable postharvest insect detection system was developed with the goal to provide low-cost capability to acoustically assess infestations in small-scale storage facilities. Electret microphones input pest insect sounds to a 32-bit microcontroller platform that digitized and stored the signals on a digital memory card transferable to a portable laptop computer. The insect sounds then were analyzed by custom-written software that matched their spectra to those of known pests. Infestations of Sitophilus oryzae (L) in 2.6-kg bags could be detected down to densities of 1.9 adults/kg in grain and Tribolium castaneum (Herbst) down to 3.8 adults/kg in flour in laboratory settings. Also, differences in the rates of sounds per insect in treatments with different numbers ranging from 5 to 50 insects suggested that the sound rates of adults of different species at different population densities may be noticeably affected by aggregation pheromones or other behaviorally active semiochemicals. Further testing is needed but previous experience with acoustic detection systems suggests that the prototype has potential for use in small storage facilities where early detection of infestations is difficult to provide.

Detecting Aedes aegypti mosquitoes through audio classification with convolutional neural networks

The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and used them to train a convolutional neural network (CNN) through supervised learning. As a feature, we used the recording spectrogram to represent the mosquito wingbeat frequency over time visually. We trained and compared three classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In our evaluation, the binary and ensemble models achieved accuracy of 97.65% (±0.55) and 94.56% (±0.77), respectively, whereas the multiclass had an accuracy of 78.12% (±2.09). The best sensitivity was observed in the ensemble approach (96.82% ± 1.62), followed by the multiclass for the particular case of Aedes aegypti (90.23% ± 3.83) and the binary (88.49% ± 6.68). The binary classifier and the multiclass classifier presented the best balance between precision and recall, with F1-measure close to 90%. Although the ensemble classifier achieved the lowest precision, thus impairing its F1-measure (79.95% ± 2.13), it was the most powerful classifier to detect Aedes aegypti in our dataset.

An annotated dataset of bioacoustic sensing and features of mosquitoes

As vectors of malaria, dengue, zika, and yellow fever, mosquitoes are considered one of the more severe worldwide health hazards. Widespread surveillance of mosquitoes is essential for understanding their complex ecology and behaviour, and also for predicting and formulating effective control strategies against mosquito-borne diseases. One technique involves using bioacoustics to automatically identify different species from their wing-beat sounds during flight. In this dataset, we collect sounds of three species of mosquitoes: Aedes Aegypti, Culex Quinquefasciatus & Pipiens, and Culiseta. These species were collected and reproduced in the laboratory of the Natural History Museum of Funchal, in Portugal, by entomologists trained to recognize and classify mosquitoes. For collecting the samples, we used a microcontroller and a mobile phone. The dataset presents audio samples collected with different sampling rates, where 34 audio features characterize each sound file, making it is possible to observe how mosquito populations vary heterogeneously. This dataset provides the basis for feature extraction and classification of flapping-wing flight sounds that could be used to identify different species.

Measurement(s)	Sound • audio feature
Technology Type(s)	bioacoustic sensing • audio board • Microphone Device • sensor • mobile phone
Factor Type(s)	species of mosquito • sampling rate
Sample Characteristic - Organism	Aedes aegypti • Culex pipiens x Culex quinquefasciatus • Culiseta
Sample Characteristic - Location	Madeira

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13034597

Biosensors Based on Mechanical and Electrical Detection Techniques

Biosensors are powerful analytical tools for biology and biomedicine, with applications ranging from drug discovery to medical diagnostics, food safety, and agricultural and environmental monitoring. Typically, biological recognition receptors, such as enzymes, antibodies, and nucleic acids, are immobilized on a surface, and used to interact with one or more specific analytes to produce a physical or chemical change, which can be captured and converted to an optical or electrical signal by a transducer. However, many existing biosensing methods rely on chemical, electrochemical and optical methods of identification and detection of specific targets, and are often: complex, expensive, time consuming, suffer from a lack of portability, or may require centralised testing by qualified personnel. Given the general dependence of most optical and electrochemical techniques on labelling molecules, this review will instead focus on mechanical and electrical detection techniques that can provide information on a broad range of species without the requirement of labelling. These techniques are often able to provide data in real time, with good temporal sensitivity. This review will cover the advances in the development of mechanical and electrical biosensors, highlighting the challenges and opportunities therein.

An investigation of Dirofilaria immitis infection and its effects on mosquito wingbeat frequencies

Each mosquito species has a different wingbeat frequency by which they attract mates. With just a brief recording (<1/10th of a second) these acoustic signatures can be analyzed to quickly determine if mosquitoes belong to a species that is known to transmit different pathogens. A recent study has shown that mobile phones are capable of capturing acoustic data from mosquito wingbeats. We examined wingbeat signatures and flight duration patterns of D. immitis infected and non-infected Aedes aegypti to determine if mobile phone recordings of wingbeat frequencies can be used to distinguish infected mosquitoes from non-infected ones. Female mosquitoes were recorded prior to and at various time points after feeding on infected or non-infected dog blood by placing individual mosquitoes into a collection vial and recording for 60 s using the Voice Memo app for iPhone 7 plus and 8. To uniformly analyze audio data, recordings were processed using a previously described automated algorithm in Python 3.0 to determine wingbeat frequency. A total of 1669 recordings were gathered, and mosquitoes were dissected to confirm the presence and number of D. immitis larvae. Our findings indicate that there was a significant effect on wingbeat frequency with an increasing number of L3 larvae. Specifically, as the number of L3, infective stage larvae increases, a decrease in wingbeat frequency is seen. However, there was no significant effect of increasing number of L1 or L2 larvae causing increasing wingbeat frequencies. The detection of a significant difference in wingbeat frequencies between mosquitoes harboring infective stage D. immitis larvae is unique and suggests the possibility of using wingbeat recordings as a tool for vector species and pathogen surveillance and monitoring.

Mosquito-Borne Diseases Emergence/Resurgence and How to Effectively Control It Biologically

Deadly pathogens and parasites are transmitted by vectors and the mosquito is considered the most threatening vector in public health, transmitting these pathogens to humans and animals. We are currently witnessing the emergence/resurgence in new regions/populations of the most important mosquito-borne diseases, such as arboviruses and malaria. This resurgence may be the consequence of numerous complex parameters, but the major cause remains the mismanagement of insecticide use and the emergence of resistance. Biological control programmes have rendered promising results but several highly effective techniques, such as genetic manipulation, remain insufficiently considered as a control mechanism. Currently, new strategies based on attractive toxic sugar baits and new agents, such as Wolbachia and Asaia, are being intensively studied for potential use as alternatives to chemicals. Research into new insecticides, Insect Growth Regulators, and repellent compounds is pressing, and the improvement of biological strategies may provide key solutions to prevent outbreaks, decrease the danger to at-risk populations, and mitigate resistance.

Technological advances in field studies of pollinator ecology and the future of e-ecology

Our review looks at recent advances in technologies applied to studying pollinators in the field. These include RFID, radar and lidar for detecting and tracking pollinators; wireless sensor networks (e.g. 'smart' hives); automated visual and audio monitoring systems including vision motion software for monitoring fine-scale pollinator behaviours over extended periods; and automated species identification systems based on machine learning that can vastly reduce the bottleneck in (big) data analysis. An improved e-ecology platform that leverages these tools is needed for ecologists to acquire and understand large spatiotemporal datasets, and thus inform knowledge gaps in environmental policy-making. Developing the next generation of e-ecology tools will require synergistic partnerships between academia and industry and significant investment in a cross-disciplinary scientific consortia.

Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors

Vector-borne Chagas disease is endemic to the Americas and imposes significant economic and social burdens on public health. In a previous contribution, we presented an automated identification system that was able to discriminate among 12 Mexican and 39 Brazilian triatomine (Hemiptera: Reduviidae) species from digital images. To explore the same data more deeply using machine-learning approaches, hoping for improvements in classification, we employed TensorFlow, an open-source software platform for a deep learning algorithm. We trained the algorithm based on 405 images for Mexican triatomine species and 1,584 images for Brazilian triatomine species. Our system achieved 83.0 and 86.7% correct identification rates across all Mexican and Brazilian species, respectively, an improvement over comparable rates from statistical classifiers (80.3 and 83.9%, respectively). Incorporating distributional information to reduce numbers of species in analyses improved identification rates to 95.8% for Mexican species and 98.9% for Brazilian species. Given the 'taxonomic impediment' and difficulties in providing entomological expertise necessary to control such diseases, automating the identification process offers a potential partial solution to crucial challenges.

The effect of global change on mosquito-borne disease

More than 80% of the global population is at risk of a vector-borne disease, with mosquito-borne diseases being the largest contributor to human vector-borne disease burden. Although many global processes, such as land-use and socioeconomic change, are thought to affect mosquito-borne disease dynamics, research to date has strongly focused on the role of climate change. Here, we show, through a review of contemporary modelling studies, that no consensus on how future changes in climatic conditions will impact mosquito-borne diseases exists, possibly due to interacting effects of other global change processes, which are often excluded from analyses. We conclude that research should not focus solely on the role of climate change but instead consider growing evidence for additional factors that modulate disease risk. Furthermore, future research should adopt new technologies, including developments in remote sensing and system dynamics modelling techniques, to enable a better understanding and mitigation of mosquito-borne diseases in a changing world.

A Novel Methodology For Recording Wing Beat Frequencies of Untethered Male and Female Aedes aegypti

Aedes aegypti is a vector of many significant arboviruses worldwide, including dengue, Zika, chikungunya, and yellow fever viruses. With vector control methodology pivoting toward rearing and releasing large numbers of insects for either population suppression or virus-blocking, economical remote (sentinel) surveillance methods for release tracking become increasingly necessary. Recent steps in this direction include advances in optical sensors that identify and classify insects based on their wing beat frequency (WBF). As these traps are being developed, there is a strong need to better understand the environmental and biological factors influencing mosquito WBFs. Here, we developed new untethered-subject methodology to detect changes in WBFs of male and female Ae. aegypti. This new methodology involves directing an ultrasonic transducer at a free-flying subject and measuring the Doppler shift of the reflected ultrasonic continuous wave signal. This system's utility was assessed by determining its ability to confirm previous reports on the effect of temperature, body size, and age on the WBFs generated from acoustic or optical-based experiments. The presented ultrasonic method successfully detected expected trends for each factor for both male and female Ae. aegypti without the need for subject manipulation and potential impediment of natural flight dynamics due to tethering. As a result, this ultrasonic methodology provides a new method for understanding the environmental and physiological determinants of male and female WBFs that can inform the design of remote mosquito surveillance systems.

Precopulatory acoustic interactions of the New World malaria vector Anopheles albimanus (Diptera: Culicidae)

Background

Anopheles albimanus is a malaria vector in Central America, northern South America and the Caribbean. Although a public health threat, An. albimanus precopulatory mating behaviors are unknown. Acoustics play important roles in mosquito communication, where flight tones allow males to detect and attract potential mates. The importance of sound in precopulatory interactions has been demonstrated in Toxorhynchites brevipalpis, Aedes aegypti, Culex quinquefasciatus and Anopheles gambiae; convergence in a shared harmonic of the wing beat frequency (WBF) during courtship is thought to increase the chance of copulation. To our knowledge, An. albimanus precopulatory acoustic behaviors have not been described to date. Here, we characterized An. albimanus (i) male and female flight tones; (ii) male–female precopulatory acoustic interactions under tethered and free flight conditions; and (iii) male-male acoustic interactions during free flight.

Results

We found significant increases in the WBFs of both sexes in free flight compared to when tethered. We observed harmonic convergence between 79% of tethered couples. In free flight, we identified a female-specific behavior that predicts mate rejection during male mating attempts: females increase their WBFs significantly faster during mate rejection compared to a successful copulation. This behavior consistently occurred during mate rejection regardless of prior mating attempts (from the same or differing male). During group flight, males of An. albimanus displayed two distinct flying behaviors: random flight and a swarm-like, patterned flight, each associated with distinct acoustic characteristics. In the transition from random to patterned flight, males converged their WBFs and significantly decreased flight area, male-male proximity and the periodicity of their trajectories.

Conclusions

We show that tethering of An. albimanus results in major acoustic differences compared to free flight. We identify a female-specific behavior that predicts mate rejection during male mating attempts in this species and show that male groups in free flight display distinct flying patterns with unique audio and visual characteristics. This study shows that An. albimanus display acoustic features identified in other mosquito species, further suggesting that acoustic interactions provide worthwhile targets for mosquito intervention strategies. Our results provide compelling evidence for swarming in this species and suggests that acoustic signaling is important for this behavior.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3648-8) contains supplementary material, which is available to authorized users.

Low-cost (<€5), open-source, potential alternative to commercial spectrophotometers

Spectrophotometry is a fundamental technique in many areas of science, with many applications and uses. The cost of spectrophotometers has acted as a barrier on the teaching and use of the technique. Here, we provide open-source plans to a 3D-printed cuvette holder with an interchangeable narrow–spectral bandwidth light-emitting diode (LED) block that can be used in conjunction with a smartphone’s ambient light sensor (ALS) to perform spectrophotometry. A Lego version with an interchangeable LED block is also presented. Results from the smartphone spectrophotometer in comparison with commercially available spectrophotometers demonstrated functionality, and the model may have many applications, especially in indirect spectrophotometry, such as in the protein assay shown here. The plans for the 3D-printed model are freely available on GitHub, as are editable files to allow customisation by users. We would encourage users to share adaptations with the scientific community.

The cost of spectrophotometers has acted as a barrier to the teaching and use of spectrophotometry; this Community Page article provides 3D printed plans for a cuvette holder with an interchangeable LED block that can be used with a smartphone’s Ambient Light Sensor (ALS) to perform spectrophotometry for less than €5.

Acoustic, Pitfall Trap, and Visual Surveys of Stored Product Insect Pests in Kenyan Warehouses

Grain production is an important component of food security in Kenya but due to environmental conditions that favor rapid growth of insect populations, farmers and other agricultural stakeholders face ongoing and novel challenges from crop and stored product pest insects. To assist development of methods to reduce economic losses from stored product insect pests in Kenya, acoustic, visual, and pitfall trap surveys were conducted in five grain storage warehouses. Two commercially available acoustic systems successfully detected the pests of greatest economic importance, Sitophilus zeamais (Motschulsky) and Prostephanus truncatus (Horn). Other insects of lesser economic importance also were observed in the visual surveys, including Sitotroga cerealella (Olivier) (Lepidoptera: Gelechiidae), and Tribolium castaneum (Herbst). This study demonstrated that the use of acoustic technology with visual surveys and pitfall traps can help managers to identify and target infestations within their warehouses, enabling them to reduce postharvest losses. With most warehouses being located in relatively noisy urban or peri-urban areas, background noise considerations are being incorporated into the design of future acoustic detectors for stored pest infestations. Kenya must import grain yearly to meet consumption needs; however, if the current yearly postharvest losses of 20-30% in warehouses decreased, import costs could be reduced considerably.

A Multispectral Backscattered Light Recorder of Insects’ Wingbeats

Most reported optical recorders of the wingbeat of insects are based on the so-called extinction light, which is the variation of light in the receiver due to the cast shadow of the insect’s wings and main body. In this type of recording devices, the emitter uses light and is placed opposite to the receiver, which is usually a single (or multiple) photodiode. In this work, we present a different kind of wingbeat sensor and its associated recorder that aims to extract a deeper representational signal of the wingbeat event and color characterization of the main body of the insect, namely: a) we record the backscattered light that is richer in harmonics than the extinction light, b) we use three different spectral bands, i.e., a multispectral approach that aims to grasp the melanization and microstructural and color features of the wing and body of the insects, and c) we average at the receiver’s level the backscattered signal from many LEDs that illuminate the wingbeating insect from multiple orientations and thus offer a smoother and more complete signal than one based on a single snapshot. We present all the necessary details to reproduce the device and we analyze many insects of interest like the bee Apis mellifera, the wasp Polistes gallicus, and some insects whose wingbeating characteristics are pending in the current literature, like Drosophila suzukii and Zaprionus, another member of the drosophilidae family.