Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25,867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are deadly vectors) is amongst the highest. We present important lessons learned and practical impact of our techniques towards the end of the paper

A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae Classification

Mosquito-borne diseases (MBDs), such as dengue virus, chikungunya virus, and West Nile virus, cause over one million deaths globally every year. Because many such diseases are spread by the Aedes and Culex mosquitoes, tracking these larvae becomes critical in mitigating the spread of MBDs. Even as citizen science grows and obtains larger mosquito image datasets, the manual annotation of mosquito images becomes ever more time-consuming and inefficient. Previous research has used computer vision to identify mosquito species, and the Convolutional Neural Network (CNN) has become the de-facto for image classification. However, these models typically require substantial computational resources. This research introduces the application of the Vision Transformer (ViT) in a comparative study to improve image classification on Aedes and Culex larvae. Two ViT models, ViT-Base and CvT-13, and two CNN models, ResNet-18 and ConvNeXT, were trained on mosquito larvae image data and compared to determine the most effective model to distinguish mosquito larvae as Aedes or Culex. Testing revealed that ConvNeXT obtained the greatest values across all classification metrics, demonstrating its viability for mosquito larvae classification. Based on these results, future research includes creating a model specifically designed for mosquito larvae classification by combining elements of CNN and transformer architecture.Comment: 6 pages, 5 figures, 4 tables. Image dataset, fine-tuning code, and pre-trained models are available at https://github.com/thenerd31/vit-cnn-mosquito-image-classificatio

Carbon dioxide sensitivity in two disjunct populations of the pitcher-plant mosquito, Wyeomyia smithii

The pitcher-plant mosquito, Wyeomyia smithii, utilizes carbon dioxide receptors primarily on their maxillary palps to seek potential hosts for blood meals. Two disjunct populations of W. smithii were analyzed to test for differences in carbon dioxide sensitivity that would correlate to varying levels of autogeny, ranging from the autogenous Northern populations (from North Carolina through Canada) to the anautonenous Southern populations (Florida – Louisiana), with the Georgia population exhibiting a shift from autogeny to anautogeny over the past two decades. I compared Georgia (Tattnall Co.) and Florida populations using blood feeding assays and olfactometry assays. Willingness to blood feed was assessed using hand-in-cage assays, and olfactometry assays were conducted using a box dual-choice olfactometer to determine decision-making when exposed to a carbon dioxide source. Results demonstrated that the Southern population was more likely to take a blood meal than the Tattnall County population and that the Southern population has a higher sensitivity to carbon dioxide than the Tattnall County population. This may be explained by differences in environmental conditions between the two habitats

Classifying dangerous species of mosquito using machine learning

This thesis begins by presenting the performance of modern Time Series Classification (TSC) approaches, including HIVE-COTEv2 & InceptionTime, on 4 new insect wingbeat datasets. The experiments throughout this thesis endeavour to explore whether it is possible to classify flying insects into their respective species and into group based on their sex. Furthermore, it is hypothesised that a hierarchical approach to classifying flying insects is possible via filtering “easy” cases using cheap to obtain features, reducing the number of times processing intensive approaches are utilised. Experiments are undertaken on 3 representations of the data: Harmonic Spectral Product (HSP), the raw data and spectral data. HSP is a method of extracting the fundamental frequency of a signal. It represents a logical benchmark for comparison and, is easy and quick to extract. In one dataset, InsectSounds, species are separated into sex. Evaluation of the results achieved with the HSP representation showed that despite a relatively poor overall accuracy this feature produces a low type II error with respect to female mosquitoes. It is shown that classes of mosquitoes species that are female were more likely to be miss-classified as other female mosquito classes and, where fly classes are miss-classified as mosquito classes, they are typically classified as male mosquitoes. Previous work had shown that transformation into the frequency domain has a positive effect on performance. Audio data is typically recorded at a high sample rate, which results in high spectral resolution. As a result, approaches from the literature have used truncation of high and low frequency data to reduce runtime. It is hypothesised that inclusion of low frequency data will aid classification. This is because low frequency data is likely caused by the body of the mosquito and morphological differences, such as size, are strongly correlated to sex. The results show that the performance of all approaches was improved by the use of spectral data. The results also showed that spectral data that included low frequency information resulted in a higher overall accuracy than transformations that discarded it. Formative experiments showed that HIVE-COTEv1 was the most accurate approach at classifying flying insects. HIVE-COTEv1 is a heterogeneous approach that consists of 4 modules, Random Interval Spectral Ensemble (RISE), Bag Of SFA Symbols (BOSS), Shapelet Transform Classifier (STC) and Time Series Forest (TSF). The predictive power of these modules are combined via Cross-validation Accuracy Weighted Probabilistic Ensemble (CAWPE). The RISE approach was chosen as the spectral component as it was “best in class” at the inception of HIVE-COTEv1. It is suggested that a significant improvement to the usability and accuracy of RISE, would translate as an improvement in the performance of HIVE-COTEv1. The introduction of contracting provided a method through witch the training time of RISE could be effectively controlled, improving its usability. A review of the interval selection procedure led to improvements that had a significant positive effect on accuracy. A review of spectral transforms and the method of combining them led to a further improvement to accuracy, and an architecture in which multiple transformations are applied. In order for smart traps to be effective they are required to work for extended periods in rural locations. Implementations of hierarchical approaches show that two expert features, HSP and time of flight (TOF) are effective in reducing test time and therefore the amount of processing required. This is achieved via first classifying the test case using simple approaches, such as BayesNet, and only if the confidence in the prediction does not meet a parameterised threshold using a more powerful approach. In an evaluation of several methods of combination, the most efficient of these is shown to increase classification accuracy by 0.6%, increase the TPR of female mosquitoes by 48/10,000, decrease the FNR of female mosquitoes by 83/15,000 and reduce test time by 1.5 hours over 25,000 instances, when compared to the single best approach InceptionTime. Furthermore, a cumulative approach to combining the expert features with the InceptionTime approach resulted in a 4.14% increase in accuracy, an increase in the TPR of female mosquitoes of 139/10,000 and a decrease in the FNR of female mosquitoes of 45/15,000

Modeling the spatial distribution of Culex and Stegomyia mosquitoes collected in the Taita Hills, Kenya in 2016, with notes on other genera

Mosquitoes are arguably amongst the most economically and socially important animals on the planet due to their ability to act as vectors for pathogens, including parasites and viruses, from animals to humans, or between humans. Mosquito-borne diseases (MBDs), are contracted following infection by one or more mosquito borne viruses (MBVs) or parasites, including dengue virus (DENV), chikungunya virus (CHIKV), Zika virus (ZIKV), West Nile virus (WNV), yellow fever virus (YFV) and malaria, and annually cause more than one million human deaths (WHO 2016). MBDs are contracted after an infected mosquito transfers one or more pathogens in the course of blood feeding from one host to another. Three important genera which act as vectors for many pathogens are Anopheles, Culex and Stegomyia and they are most problematic in the tropical and subtropical regions of Asia, South America and Africa (WHO 2016). Among vector-borne diseases (VBDs), MBDs have the strongest dependence on environmental factors. These factors have either direct or indirect impact on mosquito presence and abundance as mosquitoes are dependent on habitat suitability. This study will utilize species distribution modeling (SDM) to investigate the relationship between environmental, anthropogenic and distance factors on the occurrence of mosquito species. It forms part of an ongoing Wildlife screening project, led by Prof. Olli Vapalahti, which aims to screen mosquitoes, rodents and bats for new and known viruses in Kenya. The absence of previous studies of the geographical distribution and habitat suitability patterns of mosquito species over the Taita Hills region in southeastern Kenya, justifies the need for this research. This project has three main objectives: 1) to investigate which mosquito genera are distributed in the Taita Hills, and how they are distributed, 2) to examine which factors best explain the presence of Culex and Stegomyia mosquitoes, 3) to test whether any of the available statistical regression models can reliably estimate the distribution of Culex and Stegomyia mosquitoes, and to build predictive maps for estimations created by the most reliable models. Biological, Geographic Information Systems (GIS) and statistical methods were combined in the study. Data consists of occurrence, environmental, anthropogenic, distance and biological data. The specimens were collected from 122 locations from January–March 2016 throughout the Taita Hills. Environmental, anthropogenic and distance data were acquired from the satellite and aerial imagery and produced in ArcMap. The biomod2 package, intended for ensemble forecasting of species distributions in R, was used to generate models. After multicollinearity of the environmental, anthropogenic and distance factors was pruned, the best estimating predictor variables were selected. The factors that best estimated the distribution of Culex were slope, human population density, NDVI, distance to roads and elevation. This resulted in six reliable models with accurate estimation values. Multivariate adaptive regression splines (MARS) resulted area under the curve (AUC)- value of 0.806, and a traditional Generalized linear model(GLM) brought an AUC- value of 0.730 with high statistical significance rates, both above the value for a good model fit (AUC ≥ 0.7); thus ensuring a reliable estimation. Five environmental, anthropogenic and distance factors best estimated the distribution of Stegomyia: mean radiation in January–March, human population density, NDVI, distance to roads and mean temperature in January–March. By these predictors, biomod2 resulted in highest AUC- values for generalized boosted model (hereafter GBM) and random forest (RF) with AUC- value of 0.708 for each. Hence, reliable estimations resulted for both Culex and Stegomyia, which are visualized by the probability of presence maps in the Results chapter. The results may be used as a guide for public health officials in the Taita region regarding the distribution, favorable habitats and prevention strategies of Culex and Stegomyia mosquitoes, which are capable of transmitting mosquito-borne infections.Hyttyset ovat yksi taloudellisesti ja sosiaalisesti merkittävimmistä eläinlajeista planeetallamme, sillä ne kykenevät välittämään taudinaiheuttajia, kuten loisia tai viruksia, eläimistä ihmisiin ja ihmisistä toisiin. Hyttysten levittämät taudit syntyvät yhden tai useamman hyttysen levittämän viruksen tai loisen aiheuttamana tartuntana. Tällaisia tartuntatauteja ovat dengue virus (DENV), chikungunya virus (CHIKV), Zika virus (ZIKV), malaria, Länsi-Niilin virus ja keltakuume, jotka ovat aiheuttaneet vuosittain yli miljoona kuolemaa maailmanlaajuisesti (WHO 2016). Hyttysten levittämät sairaudet syntyvät, kun tartunnan saanut hyttynen siirtää yhden tai useamman taudinaiheuttajan isännästä toiseen veren imemisen aikana. Kolme hyttyssukua; Anopheles, Culex ja Stegomyia (Aedes), toimivat merkittävimpinä taudinaiheuttajien välittäjinä synnyttäen ongelmallisimman tilanteen erityisesti Aasian, Etelä-Amerikan ja Afrikan trooppisilla ja subtrooppisilla alueilla (WHO 2016). Vektorien välittämistä taudeista, hyttysten levittämät taudit ovat läheisimmin yhteydessä ihmistoimintaan liittyviin tekijöihin sekä ympäristötekijöihin. Ympäristötekijöillä on joko suora tai epäsuora vaikutus hyttysten esiintymiseen, sillä hyttyset ovat riippuvaisia suotuisasta elinympäristöstä. Tämä tutkimus hyödyntää lajilevinneisyysmallinnusta hyttyshavaintojen, ympäristömuuttujien ja ihmistoimintaan liittyvien muuttujien välisten suhteiden tarkastelussa. Tämä tutkimus on osa prof. Olli Vapalahden luotsaamaa Villieläinten seulonta-projektia, jonka tavoitteena on löytää uusia lajeja ja etsiä mahdollisia viruksia jyrsijöistä, lepakoista ja hyttysistä Keniassa. Hyttyslajien maantieteelliseen levinneisyyteen ja elinympäristöyhteyksiin liittyvien aiempien tutkimusten puuttuminen vahvistaa tarvetta lisätutkimukselle Taita Hillsin alueella Kaakkois-Keniassa. Tutkimuksella on kolme päätavoitetta: 1) tutkia, mitä hyttyssukuja Taita Hillsin alueella esiintyy, ja miten kerättyjen hyttyssukujen levinneisyys sijoittuu alueellisesti 2) tarkastella, mitkä tekijät selittävät parhaiten Culex ja Stegomyia hyttysten levinneisyyttä, 3) antaa vastaus hypoteesiin; voiko jokin tilastollinen malli ennustaa uskottavasti Culex ja Stegomyia hyttysten levinneisyyttä. Mahdollisten luotettavien mallien avulla on lisäksi tarkoitus ennustaa hyttyslajien levinneisyyttä ennustekartoin. Tässä tutkimuksessa yhdistettiin biologisia, tilastollisia, ja paikkatietojärjestelmiin perustuvia tutkimusmetodeita. Tutkimusaineisto sisältää havaintoaineiston, ympäristöaineiston, ihmistoimintaan ja etäisyyksiin perustuvan aineiston sekä biologisen aineiston. Näytteitä kerättiin yhteensä 122 sijainnista Taita Hillsin alueella tammi-maaliskuussa 2016. Ympäristöaineisto sekä ihmistoimintaan ja etäisyyksiin perustuvat aineistot saatiin satelliitti- ja ilmakuvista, ja ne tuotettiin ja muokattiin ArcMap- ohjelmassa. Analyysissä käytettiin biomod2- ohjelmapakettia, joka on lajilevinneisyyden ennustamiseen tarkoitettu alusta R-ohjelmointiympäristössä. Selittävien muuttujien eli ennustemuuttujien korrelaatioiden testauksen jälkeen parhaiten ennustavat muuttujat valittiin lopulliseen malliin. Parhaiten Culexin levinneisyyttä ennustavia tekijöitä olivat rinnekaltevuus, asukastiheys, NDVI, etäisyys tiehen sekä korkeus. Tämä tuotti 6 luotettavaa ennustemallia korkeilla ennustearvoilla. Multivariate adaptive regression splines (MARS) tuotti AUC(Area under curve)-arvon 0.806, ja perinteinen yleistetty lineaarinen malli(GLM) tuotti AUC-arvon 0.730 tilastollisesti merkitsevillä arvoilla. Kumpikin malli sai hyvän mallin sovittamisen ylittävän AUC-arvon (AUC ≥ 0.7), ja tuotti näin luotettavan ennusteen Culex ja Stegomyia hyttysten lajilevinneisyydelle. Stegomyia- hyttysten levinneisyyttä ennusti parhaiten viisi ennustemuuttujaa mukaan lukien keskisäteily, asukastiheys, NDVI, etäisyys tiehen sekä keskilämpötila. Näillä muuttujilla, korkeimmat AUC-arvot tuotti yleistetty luokittelupuumenetelmä (GBM) ja satumetsä(RF), AUC-arvoilla 0.708. Kummallekin hyttyssuvulle, Culexille ja Stegomyialle syntyi luotettavia levinneisyysennusteita, jotka esitetään todennäköisyyskarttoina Results-osiossa. Tutkimuksen tuloksia voidaan hyödyntää terveysviranomaisten ohjenuorana hyttysperäisiä tauteja levittävien Culex ja Stegomyia hyttysten suotuisten elinympäristöjen kartoittamisessa, sekä niiden esiintymiseen ja tautien ehkäisyyn liittyvien strategioiden tukena Taita Hillsin alueella

Classifying Flies Based on Reconstructed Audio Signals

Advancements in sensor technology and processing power have made it possible to create recording equipment that can reconstruct the audio signal of insects passing through a directed infrared beam. The widespread deployment of such devices would allow for a range of applications previously not practical. A sensor net of detectors could be used to help model population dynamics, assess the efficiency of interventions and serve as an early warning system. At the core of any such system is a classification problem: given a segment of audio collected as something passes through a sensor, can we classify it? We examine the case of detecting the presence of fly species, with a particular focus on mosquitoes. This gives rise to a range of problems such as: can we discriminate between species of fly? Can we detect different species of mosquito? Can we detect the sex of the insect? Automated classification would significantly improve the effectiveness and efficiency of vector monitoring using these sensor nets. We assess a range of time series classification (TSC) algorithms on data from two projects working in this area. We assess our prior belief that spectral features are most effective, and we remark on all approaches with respect to whether they can be considered ``real-time''

Deep learning in population genetics

KK is supported by a grant from the Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE), GSC 81, within the project GENOMIE QADOP. We acknowledge the support of Imperial College London - TUM Partnership award.Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, con volutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.Publisher PDFPeer reviewe

The citizen science project ‘Mückenatlas’: contributions of opportunistic data collection to mosquito research in Germany

Citizen science – the involvement of the public in scientific research – has become an emerging field, both as a research approach and as a discipline (the science of citizen science) by itself. The ‘Mückenatlas’ (German for ‘mosquito atlas’) was launched in 2012, shortly before citizen science also gained momentum in Germany. The goal of the ‘Mückenatlas’ is to support mapping the occurrence and distribution of native and introduced mosquito species. Therefore, people collect and submit physical mosquito samples to the responsible research institutions. In return, participants receive an individual answer with information about the biology of the captured spe¬cies and, if desired, a personal marker on the collectors’ map on the ‘Mückenatlas’ website. In this thesis, the project was evaluated from three perspectives, based on current controversies in citizen science: as a monitoring method, as a data source, and as a public outreach activity. The general aim of the dissertation was to assess the contributions of the opportunistic data collection of the ‘Mückenatlas’ project to mosquito research in Germany. The ‘Mückenatlas’ performance as monitoring method was evaluated by comparing it to a professional monitoring approach. The results showed that monitoring by professionals allows for a better coverage of land use types and species richness, whereas the citizen science approach provides important data from urban areas and can very well detect invasive species. By investigating the ‘Mückenatlas’ data collection as data source for research, anthropogenic and environmental factors were identified as drivers of the spatio-temporal variation in the numbers of submissions. Despite this bias, a study of the effects of urbanisation on indoor mosquito communities showed that opportunistic data have the capacity to confirm findings and generate novelty. Finally, considering the ‘Mückenatlas’ as an outreach activity demonstrated the positive association of mass media reports with the number of submissions across time and space. In addition, the style of the titles and texts of media reports, as well as an already raised media and public attention towards mosquito topics, increased the responsiveness of participants. The findings of this thesis show that the opportunistic data collection of the ‘Mückenatlas’ can make a crucial contribution to mosquito research, especially in gaining insights into species occurrence through the sheer number of samples submitted. Recommendations are made on when and how citizens can be involved in formal mosquito monitoring programmes, what biases and patterns to consider in data analysis, and how communication strategies can influence participation and affect the data