Photonic monitoring of atmospheric fauna

Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. This doctoral work explores the potential of entomological photonic sensors to unlock some of the limitations of traditional methods. This work focuses on the development of optical instruments similar in essence to lidar systems, with the goal of counting and identifying flying insects from a distance in their natural habitat. Those systems rely on the interactions between the near-infrared laser light and insects flying through the laser beam. Each insect is characterized by retrieving its optical and morphological properties, such as wingbeat frequency, optical cross sections, or depolarization ratios. This project ran in parallel a series of laboratory and field experiments. In the laboratory, prototypes were tested and used to create a database of insects’ properties. The data were used to train machine learning classifiers aiming at identifying insects from optical signals. In the case of mosquitoes, the sex and species of an unknown specimen was predicted with a 99% and 80% accuracy respectively. It also showed that the presence of eggs within the abdomen of a female mosquito could be detected from several meters away with 87% accuracy. In the field, instruments were deployed in real-world conditions for a total of 520 days over three years. More than a million insects were observed, allowing to continuously monitor their aerial density over months with a temporal resolution down to the minute. While this approach remains very new, this work demonstrated that photonic sensors could become a powerful tool to tackle the current lack of data in the field of entomology

Signal classification by similarity and feature extraction allows an important application in insect recognition

Insects have a strong relationship with the humanity, in both positive and negative ways. It is estimated that insects, particularly bees, pollinate at least twothirds of all food consumed in the world. In contrast, mosquito borne diseases kill millions of people every year. Due to such a complex relationship, insect control attempts must be carefully planned. Otherwise, there is the risk of eliminating beneficial species, such as the recent threat of bee extinction. We are developing a\ud novel sensor as a tool to control disease vectors and agricultural pests. This sensor captures insect flight information using laser light and classify the insects according to their species. Therefore, the sensor will provide real-time population estimates of species. Such information is the key to enable effective alarming systems for outbreaks, the intelligent use of insect\ud control techniques, such as insecticides, and will be the heart of the next generation of insect traps that will capture only species of interest. In this paper, we demonstrate how we overtook the most importante challenge to make this sensor practical: the creation of accurate classification systems. The sensor generates\ud a very brief signal as result of the instant that the insect crosses the laser. Such events last for tenths of a second and have a very simple structure, consequence of the wings movements. Nevertheless, we managed to successfully identify relevant features using speech and audio analysis techniques. Even with the described challenges, we show that we can achieve an accuracy of 98% in the task of disease vector mosquitoes identification.São Paulo Research Foundation (FAPESP) (Grants #2011/04054-2 and #2012/50714-7

An Innovative Deep Learning Method to Diagnose Mosquito-Borne Illnesses in Blood Image Analysis

Introduction: Malaria, an infectious illness carried by the bite of infected mosquitoes, is a significant public health concern, especially in Africa. The management of mosquito-human contact is crucial to mitigate its transmission. Artificial intelligence, including machine learning and deep learning techniques, is being utilized to enhance the diagnosis and identification of mosquito species. This advancement aims to facilitate the development of more efficient control measures. Aims and Objective: To analyze the efficiency of three deep learning models in identifying blood-borne diseases by evaluating the macro and micro picture of blood samples. Method: In this retrospective investigation, three deep learning algorithms, namely Convolutional Neural Networks (CNN), MobileNetV2, and ResNet50, were used to identify mosquito-borne illnesses, focusing on malaria. The research used a dataset of 120 blood samples gathered over one year from the hospital's pathology department. The CNN model streamlines preprocessing with multilayer perceptrons, simplifying malaria component extraction. MobileNetV2, a lightweight network, outperforms others with fewer parameters. Its compact blocks in Dense-MobileNet models minimize constraints and computation expenses. ResNet50 resolves degradation issues with a residual structure, preventing overfitting as hidden layers increase. Results: The study evaluated three deep learning models (CNN, MobileNetV2, and ResNet50) for medical classification. The study also demonstrated improved True Positive Rates as False Positive Rates increased, indicating better accurate identification while controlling false positives. ResNet50 consistently outperformed the other models, showcasing its superior performance. The study revealed high precision scores for all models, classifying "Uninfected" and "Infected" cases. ResNet50 exhibited slightly higher precision, indicating its precision-based superiority. Overall, all models demonstrated vital accuracy, and ResNet50 showed exceptional performance. The study found that ResNet50 performs better in True Positive and False Positive Rates. Conclusion: The study has concluded that ResNet50 has shown the best performance in detecting blood-borne diseases

Towards a portable mid-infrared tool for analysis of mosquito vectors of malaria

Mid-infrared spectroscopy (MIRS) has emerged as a potential tool to predict species, age and infection in the Anopheles malaria mosquitoes as well as in other disease vectors. The main advantages of optical methods in general are their speed, little or no sample preparation, label-free, lower cost and already established protocols and analysis pipelines. New rapid, low cost, high-throughput tools for vector surveillance are urgently needed to develop and optimise new vector control strategies, as vector borne diseases (VBD) are spreading around the globe due to climate change and globalisation, and endemic countries are suffering resurgence of malaria cases following weakening of control tools. However, the current commercially available FTIR spectrometers have limitations. They are expensive, bulky and low power that hider its implementation in the field. Quantum cascade lasers (QCL) have become an alternative to FTIR light sources due to their unique characteristics (i.e. coherence, high power in a smaller spot size, small chip size), which allows easier implementation for the field due to its lower cost, practicality, and accuracy. These characteristics can expand the possibilities to develop new ways to measure spectral information from disease vectors. This thesis is aimed at developing a QCL-based spectrometer, understanding the most informative infrared region for VBD surveillance and the use of legs for surveillance and prediction of key traits from mosquitoes using MIRS. In this project, micro-diffuse reflectance spectroscopy (µDRIFT) was used on mosquito legs to predict age, species and cuticular insecticide resistance. Indeed, spectra from legs led to high accuracy ML models for age prediction (overall model accuracy: 77.1% (± 6.5%) with a mean accuracy of 82% for 3 days old and 74% for 10 days old) and moderate accuracy for species identification (overall model accuracy: 69.1% (± 7.9%) with a mean accuracy of 68% for An. gambiae and 71% for An. coluzzii). Finally, cuticular resistance in three strains of Anopheles mosquitoes was identified with high accuracy when grouped into susceptible and resistant classes (overall model accuracy: 71.3% (± 8%) with a mean accuracy of 73% for susceptible and 71% for resistant class). However, these preliminary findings need to further be confirming by ruling out confounding factors such as the use of different strains of Anopheles by using a single strain with various degrees of insecticide resistant. I found that Partial Least Squares Discriminant Analysis (PLS-DA) and can be used for high accuracy prediction between An. gambiae and An. coluzzii when tested on laboratory samples from the same origin (mean accuracy: 87%). However, species prediction decreases when the model is tested on samples from different laboratories (mean accuracy: 62%) and in semi-field samples (mean accuracy: 46.5%). For age prediction, PLS regression was able to predict different group ages (3, 5, 7, 9, 12, 15 days old) when tested with laboratory samples from the same origin (R2 = 0.68, RMSE = 2.24) and with samples from other laboratories (R2 = 0.78, RMSE = 1.89). Nevertheless, the model cannot predict the age of semi-field samples (R2= -1.84, RMSE = 7.99). Also, I found narrower spectral windows of ≈ 300 cm−1 in length located on the Amide I and Amide II region are sufficient to predict mosquito species using machine learning (accuracy from 88% to 98%). This can help for a more efficient way of collecting spectral data. Future work should focus on how to improve model calibration by adding samples with diverse origin (different laboratories, different rearing conditions) to improve model generalisation. Finally, I have developed a QCL-based spectrometer in the range of 8-11 µm with scan speeds up to 500 Hz, with a maximum tuning rate of 400 µm/s. The system can collect spectra from polymers (polypropylene, polyethylene terephthalate and polyethylene) and biological samples (mosquitoes) in transmission mode. When compared to commercial FTIRs, MIRS measurements of whole mosquito bodies in KBr discs through the QCL-based spectrometer were in high agreement at bands 988, 1029 and 1056 cm−1 showing that the newly developed device works in mosquitoes. This study has made the first step towards the use of QCL-based system for spectroscopy of insect disease vectors, opening new opportunities for the implementation and use of midinfrared spectroscopy for vector-borne disease surveillance

Earth observation and mosquito-borne diseases: assessing environmental risk factors for disease transmission via remote sensing data

Despite global intervention efforts, mosquito-borne diseases remain a major public health concern in many parts of the world. New strategies to target interventions rely on spatially explicit information about disease transmission risk. Because the transmission of mosquito borne diseases is influenced by environmental conditions, environmental data are often used to predict disease risk. However, the relationships between environmental conditions and such diseases are not homogeneous across different landscapes and requires a context-dependent understanding. The research presented in this dissertation consists of three case studies that used remote sensing data to identify environmental risk factors for mosquito-borne diseases in different geographic settings. In the first project, the distribution of malaria cases in two study areas in the Amhara region of Ethiopia was analyzed with the help of remote sensing data on land surface temperature, precipitation, spectral indices, as well as land cover and water availability. Environmental variables were derived from remote sensing data and their relationships with spatial and temporal patterns of malaria occurrence were investigated. Settlement structure played an important role in malaria occurrence in both study areas. Climatic factors were also important, with relative risk following a precipitation gradient in the area by lake Tana and a temperature gradient along the Blue Nile River escarpment. This research suggests that studies aiming to understand malaria-environmental relationships should be geographically context specific so they can account for such differences. Second, the spatial distribution of West Nile virus (WNV) risk in South Dakota was studied via different geospatial environmental datasets. We compared the effectiveness of 1) land cover and physiography data, 2) climate data, and 3) spectral data for mapping the risk of WNV transmission. The combination of all data sources resulted in the most accurate predictions. Elevation, late season (July/August) humidity, and early-season (May/June) surface moisture were the most important predictors of disease distribution. Indices that quantified inter-annual variability of climatic conditions and land surface moisture were better predictors than inter-annual means. These results suggest that combining measures of inter-annual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk. Third, mosquito populations in Norman, Oklahoma, were analyzed to investigate the influences of land cover and microclimate on the abundance of vector mosquitoes in a heterogeneous urban environment. Remotely-sensed variables, microclimate measurements, and weather station data were used to study patterns of mosquito abundances. Spatial distributions of the two vector species Ae. albopictus and Cx. quinquefasciatus were strongly associated with land cover variables. Impervious surface area positively affected the abundance of both species. Canopy cover was positively associated with the abundance of Cx. quinquefasciatus but negatively with Ae. albopictus abundance. Among all models based on time-varying environmental data, those based on remotely-sensed variables performed best in predicting species abundances. Abundances of both species were positively associated with high temperature and high relative humidity on the trap day, but negatively associated with precipitation two weeks prior to trapping. These results emphasize the great potential for including satellite imagery in habitat analyses of different vector mosquitoes. The results presented in this dissertation contribute to the understanding of how land cover and geographic context influence the transmission of mosquito-borne diseases. Particularly remote sensing variables capturing static land cover conditions and dynamic measures of vegetation greenness and moisture can explain spatial variation in disease transmission. as well as vector mosquito distribution. Whereas remotely sensed climatic variables like temperature and precipitation influenced gradients in malaria cases at a regional scale, they explained mostly seasonal variation in mosquito distribution at a city scale. Over-all, freely available remote sensing data can help us understand the environmental determinants of disease distribution and can be a valuable tool for predicting disease dynamics on a landscape scale

Long-Range Dispersal Behaviour and Spatial Distribution Modelling of Adult Mosquitoes in the Winnipeg Region

Mosquitoes are present in virtually every nation worldwide, acting as both a vector for many serious pathogens, and as a nuisance because of their blood-feeding behaviours. As a principle of Integrated Mosquito Management (IMM), pre-emptive rather than reactive mosquito control measures are recommended and have been shown to be effective in suppressing mosquito populations. However, pre-emptive actions require insight into the spatial dynamics of mosquitoes to be effective, as mosquito dispersal behaviour is broadly influenced by local environmental conditions and species physiology. This research project was designed to investigate the dispersal behaviour and landscape ecology of adult mosquitoes in the Winnipeg region in central Canada. In Manitoba, mosquitoes primarily present an annoyance rather than a public health risk, though a risk of exposure to several mosquito-borne diseases persists in southern Manitoba. As part of the Winnipeg’s long-standing IMM program, the city maintains a mosquito control buffer zone extending approximately 10 km beyond the city limits. Within this zone, a surveillance program is implemented for adult and larval mosquitoes and larviciding operations occur when necessary. However, there is a lack of local evidence to justify the size of this zone, and literature addressing the establishment of effective buffer zones for mosquito control is nearly non-existent. Assessment of the potential effectiveness of a buffer zone requires information on mosquito flight ranges and dispersal behaviours, as well as knowledge of their overall distribution. This study 1) demonstrates two approaches that are commonly used to characterize mosquito behaviour for the purpose of optimizing control measures: mark-release-recapture (MRR) experiments and spatial distribution modelling, and 2) uses the results of these approaches to infer the potential effectiveness of the mosquito control buffer zone surrounding Winnipeg. Mark-release-recapture studies have long been used to measure mosquito flight distances and to investigate the environmental drivers of dispersal movement. In this study, field-sourced larval mosquitoes were reared to adulthood and marked with fluorescent dust prior to release. I hypothesized that mosquitoes would actively disperse towards areas with higher moisture profiles, such as those with dense canopy cover or near bodies of water. Additionally, I predicted that females would orientate toward areas with high densities of their preferred hosts. With Winnipeg mosquito buffer in mind, these experiments were designed to a) establish the flight ranges of common mosquito species, and b) discern the influence of landscape-based variables on adult mosquitoes dispersing into the urban areas of Winnipeg from the peri-urban outskirts. Recaptured mosquitoes included primarily Aedes vexans, Culiseta inornata, and Coquillettidia perturbans, all of which were recaptured 3 km or more from a single release site located on the southern edge of the City of Winnipeg within a few days of release. Female Ae. vexans were found to commonly travel more than 3 km following release, and male recaptures were often observed several kilometres from the release site. A few female Ae. vexans were recaptured over 15 km away, but the lifetime flight range for most (90%) is estimated to be over 8 km. Female Cs. inornata movement was in part influenced by the presence of mammalian livestock. While too few Cq. perturbans were recaptured to estimate mean flight distance or total flight range, in two separate events, marked individuals were recaptured over 26 km from the release site. Generally, mosquitoes appeared to be more prevalent in areas with extensive vegetative cover, and findings suggest these areas may act as corridors that facilitate dispersal into urban areas. Remote sensing data and spatial models created with Geographic Information Systems (GIS) platforms are increasingly being used to help clarify landscape-wide patterns of mosquito distribution. The approach presented here used nine consecutive years of trap data from Winnipeg’s mosquito surveillance program to model mosquito distribution in unsampled areas. Landscape-based variables such as distance to nearest river, land cover and land use classes, as well as vegetation and wetness indices were extracted for the study area using Sentinel-2 satellite imagery at a resolution of 10 m. Circular zonal areas (“buffer zones”, though not to be confused with the mosquito control buffer zone surrounding Winnipeg) generated at varying distances around traps were used to characterize habitats in terms of these variables. These were then used as explanatory variables in random forest regressions iterated to identify key predictors of mosquito distribution. The maps produced from the final models identified “hotspots” within the Winnipeg area several common local mosquito species. I hypothesized that mosquito their preferred hosts. With Winnipeg mosquito buffer in mind, these experiments were designed to a) establish the flight ranges of common mosquito species, and b) discern the influence of landscape-based variables on adult mosquitoes dispersing into the urban areas of Winnipeg from the peri-urban outskirts. Recaptured mosquitoes included primarily Aedes vexans, Culiseta inornata, and Coquillettidia perturbans, all of which were recaptured 3 km or more from a single release site located on the southern edge of the City of Winnipeg within a few days of release. Female Ae. vexans were found to commonly travel more than 3 km following release, and male recaptures were often observed several kilometres from the release site. A few female Ae. vexans were recaptured over 15 km away, but the lifetime flight range for most (90%) is estimated to be over 8 km. Female Cs. inornata movement was in part influenced by the presence of mammalian livestock. While too few Cq. perturbans were recaptured to estimate mean flight distance or total flight range, in two separate events, marked individuals were recaptured over 26 km from the release site. Generally, mosquitoes appeared to be more prevalent in areas with extensive vegetative cover, and findings suggest these areas may act as corridors that facilitate dispersal into urban areas. Remote sensing data and spatial models created with Geographic Information Systems (GIS) platforms are increasingly being used to help clarify landscape-wide patterns of mosquito distribution. The approach presented here used nine consecutive years of trap data from Winnipeg’s mosquito surveillance program to model mosquito distribution in unsampled areas. Landscape-based variables such as distance to nearest river, land cover and land use classes, as well as vegetation and wetness indices were extracted for the study area using Sentinel-2 satellite imagery at a resolution of 10 m. Circular zonal areas (“buffer zones”, though not to be confused with the mosquito control buffer zone surrounding Winnipeg) generated at varying distances around traps were used to characterize habitats in terms of these variables. These were then used as explanatory variables in random forest regressions iterated to identify key predictors of mosquito distribution. The maps produced from the final models identified “hotspots” within the Winnipeg area several common local mosquito species. I hypothesized that mosquito populations would be densest in areas with high moisture profiles, such as vegetated regions near rivers, as was implied by the MRR experiment outcomes. Hotspots for Ae. vexans and Cx. restuans confirmed this hypothesis, as these were clustered within riparian areas closest to rivers. Conversely, population hotspots for Cx. tarsalis were located near or beyond city limits. No consistent trends were identified for Ae. dorsalis. Influence of certain vegetation-based land cover classes such as cultivated land, grass and forest were more important in predicting mosquito distribution at larger scales (500 to 1000 m) in comparison to land use classes such as commercial or industrial areas which influenced mosquito distributions at smaller scales (50 to 100 m). These support the hypothesis that relationships between mosquitoes and their surroundings extend beyond the reach of their olfactory or visual senses and that larger scale landscape factors also influence mosquito movement to a significant extent. Based on these findings, we can infer that many mosquito species are capable of dispersing distances that justify the extent of the current buffer zone surrounding the Winnipeg city limits. The results from the MRR experiments demonstrated that these mosquitoes could disperse several kilometres into the city from outside its limits. However, the MRR experiments were not designed to fully address the directionality of their movements, and mosquitoes may be dispersing towards rural areas as well. The results of both the spatial distribution models and the MRR experiments provided similar insights regarding habitat preferences. Most prominently, the distribution and dispersal patterns of Ae. vexans suggests that mosquitoes may be using vegetated riverbanks as corridors for dispersal. Additionally, the risk of encountering the medically important species Cx. tarsalis increases with greater distances from the city center. Both these findings indicate that the diversity in habitat preference for Winnipeg’s mosquitoes would necessitate thorough monitoring and treatment of larval habitats to prevent most mosquitoes from immigrating into the city. This may be impractical, nay impossible given the nature of the breeding habitats preferred by Ae. vexans (soils with intermittent flooding) and Cx. tarsalis (small and discrete artificial containers) and the difficulty associated with treating these. Regardless, these results illustrate an improved understanding of the landscape ecology of mosquitoes in the Winnipeg region.City of Winnipeg, Manitoba Graduate ScholarshipMaster of Science in Bioscience, Technology and Public Polic