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Spectrograms of Cx.quinquefaciatus species (a-b) and Ae.aegypti species (c-d) at 96 and 8 kHz sampling rates. Cx.quin refers to Cx.quinquefasciatus species. The blue lines represent the tracked f0
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Mosquito vector-borne diseases such as malaria and dengue constitute some of the most serious public health burdens in tropical and subtropical countries. Effective targeting of disease control efforts requires accurate estimates of mosquito vector population density. The traditional, and still most common, approach to this involves the use of trap...
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Background
Aedes aegypti , the main vector of dengue, yellow fever, and other arboviruses thrives in tropical and subtropical areas around the globe putting half of the world’s population at risk. Despite aggressive efforts to control the transmission of those viruses, an unacceptable number of cases occur every year, emphasizing the need to develo...
Citations
... Li et al. [66] managed to classify five species of mosquitoes based on their sounds, with a success rate of 73%. Similarly, Yin et al., [67] successfully detected and classified several mosquito species with wingbeat sounds using computational techniques. Folliot et al. [68] also monitored pollination by insects and tree use by woodpeckers with acoustics methods and artificial intelligence. ...
Global pollinator decline urgently requires effective methods to assess their trends, distribution and behaviour. Passive acoustics is a non-invasive and cost-efficient monitoring tool increasingly employed for monitoring animal communities. However, insect sounds remain highly unexplored, hindering the application of this technique for pollinators. To overcome this shortfall and support future developments, we recorded and characterized wingbeat sounds of a variety of Iberian domestic and wild bees and tested their relationship with taxonomic, morphological, behavioural and environmental traits at inter- and intra-specific levels. Using directional microphones and machine learning, we shed light on the acoustic signature of bee wingbeat sounds and their potential to be used for species identification and monitoring. Our results revealed that frequency of wingbeat sounds is negatively related with body size and environmental temperature (between-species analysis), while it is positively related with experimentally induced stress conditions (within-individual analysis). We also found a characteristic acoustic signature in the European honeybee that supported automated classification of this bee from a pool of wild bees, paving the way for passive acoustic monitoring of pollinators. Overall, these findings confirm that insect sounds during flight activity can provide insights on individual and species traits, and hence suggest novel and promising applications for this endangered animal group.
This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.
... Therefore, it is unsurprising that they constituted 100% of the parasitic taxa in our review. DL models to identify mosquitoes had accuracy estimates reaching 100% (Kiskin et al., 2020;Yin et al., 2023;Zhang et al., 2017). ...
Insects play vital ecological roles; many provide essential ecosystem services while others are economically devastating pests and disease vectors. Concerns over insect population declines and expansion have generated a pressing need to effectively monitor insects across broad spatial and temporal scales. A promising approach is bioacoustics, which uses sound to study ecological communities. Despite recent increases in machine learning technologies, the status of emerging automated bioacoustics methods for monitoring insects is not well known, limiting potential applications.
To address this gap, we systematically review the effectiveness of automated bioacoustics models over the past four decades, analysing 176 studies that met our inclusion criteria. We describe their strengths and limitations compared to traditional methods and propose productive avenues forward.
We found automated bioacoustics models for 302 insect species distributed across nine Orders. Studies used intentional calls (e.g. grasshopper stridulation), by‐products of flight (e.g. bee wingbeats) and indirectly produced sounds (e.g. grain movement) for identification. Pests were the most common study focus, driven largely by weevils and borers moving in dried food and wood. All disease vector studies focused on mosquitoes. A quarter of the studies compared multiple insect families.
Our review illustrates that machine learning, and deep learning in particular, are becoming the gold standard for bioacoustics automated modelling approaches. We identified models that could classify hundreds of insect species with over 90% accuracy. Bioacoustics models can be useful for reducing lethal sampling, monitoring phenological patterns within and across days and working in locations or conditions where traditional methods are less effective (e.g. shady, shrubby or remote areas). However, it is important to note that not all insect taxa emit easily detectable sounds, and that sound pollution may impede effective recordings in some environmental contexts.
Synthesis and applications: Automated bioacoustics methods can be a useful tool for monitoring insects and addressing pressing ecological and societal questions. Successful applications include assessing insect biodiversity, distribution and behaviour, as well as evaluating the effectiveness of restoration and pest control efforts. We recommend collaborations among ecologists and machine learning experts to increase model use by researchers and practitioners.
... Over recent years, there has been an increasing number of studies aimed at taxonomically classifying mosquitoes and other attributes of mosquito biology using either acoustic [9][10][11][12] or optical sensors [13][14][15][16][17][18][19], which take advantage of insect bioacoustic properties. The study of these properties, especially the mosquito flight tone or wing beat frequency, has been used for mosquito characterization and classification purposes since the 1940s [20,21]. ...
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.
Graphical Abstract
... 84,85 In addition, an effective alternative approach involves using software to detect and classify mosquito wingbeat sounds, leading to species identification. 86 These two methods play a vital role in the arbovirus surveillance of mosquitoes, as they help reduce costs, speed up the diagnostic process, and promote their widespread adoption. Other techniques for storage, preservation, and detection of genetic material are discussed in this section. ...
Arboviruses cause millions of infections each year; however, only limited options are available for treatment and pharmacological prevention. Mosquitoes are among the most important vectors for the transmission of several pathogens to humans. Despite advances, the sampling, viral detection, and control methods for these insects remain ineffective. Challenges arise with the increase in mosquito populations due to climate change, insecticide resistance, and human interference
affecting natural habitats, which contribute to the increasing difficulty in controlling the spread of arboviruses. Therefore, prioritizing arbovirus surveillance is essential for effective epidemic preparedness. In this review, we offer a concise historical account of the discovery and monitoring of arboviruses in mosquitoes, from mosquito capture to viral detection. We then analyzed the advantages and limitations of these traditional methods. Furthermore, we investigated the potential
of emerging technologies to address these limitations, including the implementation of next-generation sequencing, paper-based devices, spectroscopic detectors, and synthetic biosensors. We also provide perspectives on recurring issues and areas of interest such as insect-specific viruses.
... The study conducted centered on the application of deep learning techniques for the identification of gender and species among mosquito vectors. While the main focus of their research was on deep learning models, the achievement of their attempts to challenge species and gender identification was largely dependent on the effective extraction and utilization of distinguishing information [16] ...
Accurate assessment of mosquito population density is crucial for the efficient management of mosquito-borne diseases such as malaria and dengue in areas affected by these vectors. Nevertheless, the traditional approach of manually counting and classifying mosquitoes through the use of traps is both laborious and expensive. This research paper presents a proposed pipeline for the identification and categorization of mosquitoes from photographs, specifically designed for low-cost Internet of Things (IoT) sensors. The pipeline aims to achieve a balance between accuracy and efficiency. Through the process of fine- tuning conventional machine learning models such as VGG16, RESNET50, and Convolutional Neural Network (CNN), a notable level of accuracy of 98% is attained. The present study highlights the potential of integrating a highly effective mosquito detection device with a convolutional neural network to offer a viable balance between precision and efficiency in the realm of mosquito identification, categorization, and quantification. Consequently, this approach has promise for improving the control and prevention of mosquito-borne illnesses.
... is not a probability and thus both the OR and RR should be provided in reporting results. • Research and development are needed into new approaches to monitoring and prediction, such has integration of human mobility in malaria prediction [52,152], mosquito monitoring using acoustic sensors [153] or images [154], and novel prediction models [149,155]. ...
Background
Several countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection. Thus, there is a need for a consolidated view of existing definitions of risk and factors considered in assessing risk to analyse the merits of risk prediction models. This systematic review examines studies of the risk of malaria in Southeast Asia with regard to their suitability in addressing the challenges of malaria elimination in low transmission areas.
Methods
A search of four electronic databases over 2010–2020 retrieved 1297 articles, of which 25 met the inclusion and exclusion criteria. In each study, examined factors included the definition of the risk and indicators of malaria transmission used, the environmental and climatic factors associated with the risk, the statistical models used, the spatial and temporal granularity, and how the relationship between environment, climate, and risk is quantified.
Results
This review found variation in the definition of risk used, as well as the environmental and climatic factors in the reviewed articles. GLM was widely adopted as the analysis technique relating environmental and climatic factors to malaria risk. Most of the studies were carried out in either a cross-sectional design or case–control studies, and most utilized the odds ratio to report the relationship between exposure to risk and malaria prevalence.
Conclusions
Adopting a standardized definition of malaria risk would help in comparing and sharing results, as would a clear description of the definition and method of collection of the environmental and climatic variables used. Further issues that need to be more fully addressed include detection of asymptomatic cases and considerations of human mobility. Many of the findings of this study are applicable to other low-transmission settings and could serve as a guideline for further studies of malaria in other regions.
... By tuning the models, their sizes are reduced by roughly 60%, but only a 3% loss in classification accuracy is observed. Furthermore, they have also shown a software pipeline for the detection and classification of mosquito wingbeat sounds using low-cost IoT devices in another study [15]. The LSTM-based method has also been tested in a few studies. ...
The potential of utilizing the features of wingbeat sound to detect different species of flying mosquitoes is explored in this paper. By analyzing the environmental sound using machine learning, it becomes possible to identify as well as classify different species of flying mosquito before it spreads mosquito-borne diseases. To accomplish the identification and classification, this paper presents a hybrid model that combines Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to analyze the audio in order to classify different species. The proposed model shows significant accuracy for detecting and classifying different species of flying mosquitoes as well as mitigate the weakness of individual models. If the hybrid model is widely used, could help to reduce the spread of mosquito-borne diseases and related fatalities.
... For example, Wei et al. (2022) used convolutional networks interspersed with self-attention layers to classify a sample set of mosquito audio samples. In another direction, attempts to obtain computationally lighter models were also explored (Yin, Haddawy, Nirandmongkol, Kongthaworn, Chaisumritchoke, Supratak, Sa-ngamuang and Sriwichai, 2021;Alar and Fernandez, 2021b;Toledo, Gonzalez, Nakano, Robles, Hernandez, Perez, Lanz and Cime, 2021;Alar and Fernandez, 2021a;Su Yin, Haddawy, Ziemer, Wetjen, Supratak, Chiamsakul, Siritanakorn, Chantanalertvilai, Sriwichai and Sa-ngamuang, 2022). Su Yin et al. (2022) proposed a technique involving audio sample rate reduction and lighter classification algorithms. ...
... In another direction, attempts to obtain computationally lighter models were also explored (Yin, Haddawy, Nirandmongkol, Kongthaworn, Chaisumritchoke, Supratak, Sa-ngamuang and Sriwichai, 2021;Alar and Fernandez, 2021b;Toledo, Gonzalez, Nakano, Robles, Hernandez, Perez, Lanz and Cime, 2021;Alar and Fernandez, 2021a;Su Yin, Haddawy, Ziemer, Wetjen, Supratak, Chiamsakul, Siritanakorn, Chantanalertvilai, Sriwichai and Sa-ngamuang, 2022). Su Yin et al. (2022) proposed a technique involving audio sample rate reduction and lighter classification algorithms. ...
In this paper, we advocate in favor of smartphone apps as low-cost, easy-to-deploy solution for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. Nevertheless, devising such a smartphone app is challenging, for many reasons, including the required maturity level of techniques for identifying mosquitoes based on features that can be captured using smartphone resources. In this paper, we identify a set of (non-exhaustive) requirements that smartphone apps must meet to become an effective tooling in the fight against Ae. aegypti, and advance the state-of-the-art with (i) a residual convolutional neural network for classifying Ae. aegypti mosquitoes from their wingbeat sound, (ii) a methodology for reducing the influence of background noise in the classification process, and (iii) a dataset for benchmarking solutions for detecting Ae. aegypti mosquitoes from wingbeat sound recordings. From the analysis of accuracy and recall, we provide evidence that convolutional neural networks have potential as a cornerstone for tracking mosquito apps for smartphones.
Sustainable reductions in African malaria transmission require innovative tools for mosquito control. One proposal involves the use of low-threshold gene drive in Anopheles vector species, where a ‘causal pathway’ would be initiated by (i) the release of a gene drive system in target mosquito vector species, leading to (ii) its transmission to subsequent generations, (iii) its increase in frequency and spread in target mosquito populations, (iv) its simultaneous propagation of a linked genetic trait aimed at reducing vectorial capacity for Plasmodium, and (v) reduced vectorial capacity for parasites in target mosquito populations as the gene drive system reaches fixation in target mosquito populations, causing (vi) decreased malaria incidence and prevalence. Here the scope, objectives, trial design elements, and approaches to monitoring for initial field releases of such gene dive systems are considered, informed by the successful implementation of field trials of biological control agents, as well as other vector control tools, including insecticides, Wolbachia, larvicides, and attractive-toxic sugar bait systems. Specific research questions to be addressed in initial gene drive field trials are identified, and adaptive trial design is explored as a potentially constructive and flexible approach to facilitate testing of the causal pathway. A fundamental question for decision-makers for the first field trials will be whether there should be a selective focus on earlier points of the pathway, such as genetic efficacy via measurement of the increase in frequency and spread of the gene drive system in target populations, or on wider interrogation of the entire pathway including entomological and epidemiological efficacy. How and when epidemiological efficacy will eventually be assessed will be an essential consideration before decisions on any field trial protocols are finalized and implemented, regardless of whether initial field trials focus exclusively on the measurement of genetic efficacy, or on broader aspects of the causal pathway. Statistical and modelling tools are currently under active development and will inform such decisions on initial trial design, locations, and endpoints. Collectively, the considerations here advance the realization of developer ambitions for the first field trials of low-threshold gene drive for malaria vector control within the next 5 years.
