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

Abstract

Transmission of Plasmodium falciparum malaria parasites occurs when nocturnal Anopheles mosquito vectors feed on human blood. In Africa, where malaria burden is greatest, 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 1. Since then, progress has stalled 2 and with insecticidal bednets losing efficacy against pyrethroid-resistant Anopheles vectors 3,4 , methods that restore performance are urgently needed to eliminate any risk of malaria returning to the levels seen prior to their widespread use throughout sub-Saharan Africa 5. 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 currently possible. Tested against wild pyrethroid-resistant Anopheles 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 WHO against resistant vectors. Mathematical

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

“…The RRS imaging approach is being used in both field and lab environments to further understand the interaction between bednets and mosquitoes, including the testing of novel designs [4]. In addition, the system is helping the definition of new protocols for commercial bednet assessment.…”

“…This effect enables triangulation and hence recovery of 3D mosquito position data [26] but requires complex calibration and extensive signal processing. In contrast, it has been demonstrated that 2D tracking provides entomologically useful information [1,2,4] and the requirement in malaria control is to rapidly test design iterations of interventions over long time periods (hours) and with sufficient repeats to give statistically reliable data. Hence, this paper targets robust 2D tracking..…”

“…Bednets treated with insecticide can be very effective in preventing transmission of these infections and in Africa, the factory-treated durable type of nets referred to as longlasting insecticidal nets (LLINs), are the most effective method available and an essential element of malaria control and elimination strategies today. Increased understanding of vector behaviour at LLINs, including how different LLIN treatments affect mosquito flight around and contact with the net surface is essential for understanding LLIN modes of action [1,2] and raises the prospect of developing more effective interventions to reduce disease transmission [3,4].…”

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

“…37 At the opposite end of the scale there is a substantial body of experimentally 38 generated data on the behavior of individual mosquitoes in experimentally controlled 39 settings such as wind tunnels and olfactometers [13]. A wide range of possible sensory 40 modalities could influence host-seeking selection behavior in these conditions and these 41 different potential stimuli have been investigated with regard to isolated CO 2 42 concentration, olfactory [14], [15], [16], visual [17], [18], auditory [19], [20] and tactile 43 sensory cues [21], or combinations thereof [22]. 44 There is a knowledge gap between individual mosquito behavior and downstream 45 effects in terms of epidemiological disease transmission that could be filled by fine 46 grained models of insect flight behavior and host interactions.…”

“…For example, modifications might include vectors with different arrival 359 patterns at the bed net, different host preferences, or the influence of a second human 360 host in the room, or of a residual insecticidal treatment on the wall. Recently, we used 361 an expanded version of the model to compare seven barrier bed net designs, which 362 allowed us to rapidly identify the most promising candidate(s) to fast track for further 363 evaluation [40].…”