Use of Artificial Intelligence on the Control of Vector-Borne Diseases

Artificial intelligence has many fields of application with an increasing computational processing power, and the algorithms are reaching human performance on complex tasks. Entomological characterization of insects represents an essential activity to drive actions to control the vector-borne diseases. Identification of the species and sex of insects is essential to map and organize the control measurements by the public health system in most areas where transmission is actively occurring. In many places in the world, the methodology done for identification of the mosquitos is by visual examination from human trained researchers or technicians. This activity is time-consuming and requires several years of experience to have skills to do the job. This chapter addresses the application of artificial intelligence for identification of mosquitos associated with vector-borne diseases. Benefits, limitations, and challenges of the use of artificial intelligence on the control of vector-borne diseases are discussed in this review

The use of artificial intelligence and automatic remote monitoring for mosquito surveillance

Mosquito surveillance consists in the routine monitoring of mosquito populations: to determine the presence/absence of certain mosquito species; to identify changes in the abundance and/or composition of mosquito populations; to detect the presence of invasive species; to screen for mosquito-borne pathogens; and, finally, to evaluate the effectiveness of control measures. This kind of surveillance is typically performed by means of traps, which are regularly collected and manually inspected by expert entomologists for the taxonomical identification of the samples. The main problems with traditional surveillance systems are the cost in terms of time and human resources and the lag that is created between the time the trap is placed and collected. This lag can be crucial for the accurate time monitoring of mosquito population dynamics in the field, which is determinant for the precise design and implementation of risk assessment programs. New perspectives in this field include the use of smart traps and remote monitoring systems, which generate data completely interoperable and thus available for the automatic running of prediction models; the performance of risk assessments; the issuing of warnings; and the undertaking of historical analyses of infested areas. In this way, entomological surveillance could be done automatically with unprecedented accuracy and responsiveness, overcoming the problem of manual inspection labour costs. As a result, disease vector species could be detected earlier and with greater precision, enabling an improved control of outbreaks and a greater protection from diseases, thereby saving lives and millions of Euros in health costs.info:eu-repo/semantics/publishedVersio

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 mosqui‑ toes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxo‑ nomical identifcation. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classifcation of mosquitoes based on their fight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the feld, which could lead to signifcant improvements in vector surveillance. Methods: A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classifcation 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 fve diferent machine learning algorithms to achieve the best model for mosquito classifcation. Results: The best accuracy results achieved using machine learning were: 94.2% for genus classifcation, 99.4% for sex classifcation of Aedes, and 100% for sex classifcation of Culex. The best algorithms and features were deep neural network with spectrogram for genus classifcation and gradient boosting with Mel Frequency Cepstrum Coefcients among others for sex classifcation of either genus. Conclusions: To our knowledge, this is the frst time that a sensor coupled to a standard mosquito suction trap has provided automatic classifcation 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.info:eu-repo/semantics/publishedVersio

The potential of bioacoustics for surveying carrion insects

Knowledge of the sequential cadaver colonization by carrion insects is fundamental for post-mortem interval (PMI) estimation. Creating local empirical data on succession by trapping insects is time consuming, dependent on accessibility/environmental conditions and can be biased by sampling practices including disturbance to decomposing remains and sampling interval. To overcome these limitations, audio identification of species using their wing beats is being evaluated as a potential tool to survey and build local databases of carrion species. The results could guide the focus of forensic entomologists for further developmental studies on the local dominant species, and ultimately to improve PMI estimations. However, there are challenges associated with this approach that must be addressed. Wing beat frequency is influenced by both abiotic and biotic factors including temperature, humidity, age, size, and sex. The audio recording and post-processing must be customized for different species and their influencing factors. Furthermore, detecting flight sounds amid background noise and a multitude of species in the field can pose an additional challenge. Nonetheless, previous studies have successfully identified several fly species based on wing beat sounds. Combined with advances in machine learning, the analysis of bioacoustics data is likely to offer a powerful diagnostic tool for use in species identification.</p

A Multispectral Backscattered Light Recorder of Insects&#8217; 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\u2019s 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\u2019s 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

Evaluation of a novel sensor system integrated into a mosquito trap to determine mosquito species, age and sex

Important vector borne zoonotic diseases are transmitted by different mosquito species. Mosquito surveillance needs expert entomologists and is time-consuming. Trap-captured mosquitoes are transported to the laboratory for counting and identification, and there are over 3,500 species of mosquitoes in the world. In order to improve mosquito surveillance, we evaluated the accuracy of a novel optoelectronic sensor prototype that captures the shadow of the mosquito while is being sucked into a trap. This is the first time that species, sex and age classification of mosquitoes is made with the forced flight condition of a commercial ventilatorbased mosquito trap, where the natural wing-beat is distorted. Culex pipiens, Aedes albopictus and Aedes aegypti were used to test the sensor. Various algorithms on different feature combinations were trained and optimized for machine learning to recognize automatically mosquitoes' sex, age and species. Our system was capable to distinguish between species and sex in terms of fundamental frequency, showing that the fundament frequency was higher in males than females and higher in mosquitoes of Aedes than in Culex genus. The system proposed in this study is useful for genus classification with accuracy values that ranged from 93.83% to 95.73%. More data and training will be necessary to optimize the sensor to better classify mosquito species of the same genus since the accuracy for Aedes genus was 76.06%. Regarding gender identification, male and female were discriminated with more than 93.11% of accuracy after machine learning techniques. This information will be important for arbovirus surveillance programs since the females are the unique implied in arbovirus transmission. The accuracy in terms of age ranged from 69.81% to 90.97%, allowing to know how old the mosquito population is, providing useful data due to the importance of the age in vector capacity which it is important to estimate the risk assessment for arbovirus diseases

Keeping track of the enemy : Flight analyses of the host-seeking malaria mosquito Anopheles gambiae s.s

Female mosquitoes can transmit pathogens to their host during blood feeding and are an important vector of human diseases such as dengue, chikungunya, filariasis and malaria. After 15 years of decline in the number of fatal malaria cases, this decline came to a halt in 2016. Growing resistance against drugs and insecticides pose a serious threat for future human health. This thesis focuses on the behaviour of host-seeking malaria mosquitoes by analysing their flight paths during their approach to different host cues. Fundamental knowledge on the role of selected host cues was acquired. In addition, studies to support successful implementation of vector control interventions were performed in both wind-tunnel settings and the semi-field in Kenya. My research demonstrates that automated tracking systems can strengthen behavioural-ecological studies on disease vectors, in addition to conventional bio-assays such as olfactometers, by providing detailed information on the approach behaviour of mosquitoes to different targets. The attraction towards the host-sensory cue CO2 was investigated in an olfactometer bioassay. Trap catches of female Anopheles gambiae s.s. were enhanced by separation of the CO2 source from the source of human skin emanations. Close-range deterrent effects of CO2 were overcome by the simultaneous presence of skin emanations. Flight path analysis of mosquitoes in a wind tunnel, showed that exposure to human odour resulted in prolonged and highly convoluted flight tracks. The combination of odour with heat was crucial to induce landings of host-seeking mosquitoes. A semi-field study in Kenya revealed that house-entering mosquitoes approached the eave of a house in a wide angle to the house at eave level, where the proportion that entered uninterruptedly (23%) spent just a few seconds around the eave area. The presence of insecticide-treated nets inside a house did not repel mosquitoes as measured by the number of house entries. At close range, in a wind tunnel, free-flight exposure of mosquitoes to deltamethrin-treated nets in combination with human odour did not reveal any (excito-) repellent effect and resulted in lower mortality rates compared to standard bioassays where contact with the treated material is enforced. The knowledge obtained on the behavioural responses of mosquitoes to host cues has indirectly affected vector control tool implementations in the field. For example, in the development of an odour-baited trap, a CO2 release pipe was included that is separated from the attractive odour plume. The role of heat was exploited in the development of a repellent bioassay and a heat source was added to another trap model. Insights in house-entry behaviour and mosquito responses to bed nets support the successful implementation of push-pull systems, installation of eave tubes or implementing house improvement operations to reduce malaria transmission. An integrated vector management approach is required to further develop existing control tools by adding and improving alternative intervention techniques.</p