Characterizing Mosquito Biting Behavior Using the BiteOscope.

The biteOscope enables the high-resolution monitoring and video recording of blood-feeding mosquitoes. Mosquito biting is induced by combining host cues, an artificial bloodmeal, a membrane, and a transparent heater in a transparent behavioral arena. Machine vision techniques enable the tracking and pose estimation of individual mosquitoes to discern behavior and resolve individual feeding events. The workflow allows multiple replicates and large amounts of imaging data to be generated rapidly. These data are suitable for downstream analysis using machine learning tools for behavioral analysis, allowing subtle behavioral effects to be characterized.

Characterizing Mosquito Biting Behavior at High Resolution.

Blood feeding is a critical event in the life cycle of female mosquitoes. In addition to providing nutrients to the mosquito, blood feeding facilitates the transmission of parasites and viruses to hosts, potentially having devastating health consequences. Our understanding of these short, yet important, bouts of behavior is incomplete. How and where a mosquito decides to bite and the success of feeding can influence the transmission of pathogens. A more thorough understanding of these processes may allow the development of interventions that reduce or prevent infections. Here, we present an overview of strategies for studying mosquito biting behavior and introduce the biteOscope, which provides an opportunity to observe and understand this behavior at unprecedented spatial and temporal resolution under tightly controlled conditions. The biteOscope combines recent advances in computer vision and automated tracking with designs for behavioral arenas and controllable artificial host cues that use low-cost and readily available materials.

A minimal 3D model of mosquito flight behaviour around the human baited bed net.

BACKGROUND: Advances in digitized video-tracking and behavioural analysis have enabled accurate recording and quantification of mosquito flight and host-seeking behaviours, facilitating development of individual (agent) based models at much finer spatial scales than previously possible. METHODS: Quantified behavioural parameters were used to create a novel virtual testing model, capable of accurately simulating indoor flight behaviour by a virtual population of host-seeking mosquitoes as they interact with and respond to simulated stimuli from a human-occupied bed net. The model is described, including base mosquito behaviour, state transitions, environmental representation and host stimulus representation. RESULTS: In the absence of a bed net and human host bait, flight distribution of the model population was relatively uniform throughout the arena. Introducing an unbaited untreated bed net induced a change in distribution with an increase in landing events on the net surface, predominantly on the sides of the net. Adding the presence of a simulated human bait dramatically impacted flight distribution patterns, exploratory foraging and, the number and distribution of landing positions on the net, which were determined largely by the orientation of the human within. The model replicates experimental results with free-flying living mosquitoes at human-occupied bed nets, where contact occurs predominantly on the top surface of the net. This accuracy is important as it quantifies exposure to the lethal insecticide residues that may be unique to the net roof (or theoretically any other surface). Number of net contacts and height of contacts decreased with increasing attractant dispersal noise. CONCLUSIONS: Results generated by the model are an accurate representation of actual mosquito behaviour recorded at and around a human-occupied bed net in untreated and insecticide-treated nets. This fine-grained model is highly flexible and has significant potential for in silico screening of novel bed net designs, potentially reducing time and cost and accelerating the deployment of new and more effective tools for protecting against malaria in sub-Saharan Africa.

Diffuse retro-reflective imaging for improved video tracking of mosquitoes at human baited bednets.

Robust imaging techniques for tracking insects have been essential tools in numerous laboratory and field studies on pests, beneficial insects and model systems. Recent innovations in optical imaging systems and associated signal processing have enabled detailed characterization of nocturnal mosquito behaviour around bednets and improvements in bednet design, a global essential for protecting populations against malaria. Nonetheless, there remain challenges around ease of use for large-scale in situ recordings and extracting data reliably in the critical areas of the bednet where the optical signal is attenuated. Here, we introduce a retro-reflective screen at the back of the measurement volume, which can simultaneously provide diffuse illumination, and remove optical alignment issues while requiring only one-sided access to the measurement space. The illumination becomes significantly more uniform, although noise removal algorithms are needed to reduce the effects of shot noise, particularly across low-intensity bednet regions. By systematically introducing mosquitoes in front of and behind the bednet in laboratory experiments, we are able to demonstrate robust tracking in these challenging areas. Overall, the retro-reflective imaging set-up delivers mosquito segmentation rates in excess of 90% compared to less than 70% with backlit systems.

Barrier bednets target malaria vectors and expand the range of usable insecticides.

Transmission of Plasmodium falciparum malaria parasites occurs when nocturnal Anopheles mosquito vectors feed on human blood. In Africa, where malaria burden is highest, bednets treated with pyrethroid insecticide were highly effective in preventing mosquito bites and reducing transmission, and essential to achieving unprecedented reductions in malaria until 2015 (ref. 1). Since then, progress has stalled2, and with insecticidal bednets losing efficacy against pyrethroid-resistant Anopheles vectors3,4, methods that restore performance are urgently needed to eliminate any risk of malaria returning to the levels seen before their widespread use throughout sub-Saharan Africa5. Here, we show that the primary malaria vector Anopheles gambiae is targeted and killed by small insecticidal net barriers positioned above a standard bednet in a spatial region of high mosquito activity but zero contact with sleepers, opening the way for deploying many more insecticides on bednets than is currently possible. Tested against wild pyrethroid-resistant A. gambiae in Burkina Faso, pyrethroid bednets with organophosphate barriers achieved significantly higher killing rates than bednets alone. Treated barriers on untreated bednets were equally effective, without significant loss of personal protection. Mathematical modelling of transmission dynamics predicted reductions in clinical malaria incidence with barrier bednets that matched those of 'next-generation' nets recommended by the World Health Organization against resistant vectors. Mathematical models of mosquito-barrier interactions identified alternative barrier designs to increase performance. Barrier bednets that overcome insecticide resistance are feasible using existing insecticides and production technology, and early implementation of affordable vector control tools is a realistic prospect.