**2. Knowledge and methodology limitations to improved vector control products and practices**

Several detailed models of malaria transmission have been independently developed over the last decade, and integrated into collaborative ensemble platforms [15–21] that have successfully informed global policy [22]. However, as these models develop and improve, further progress is increasingly limited by lack of knowledge rather than global mathematical capacity:

*Differences in the predicted impact size arise due to the different assumptions made about malaria transmission in each model, which represent realistic uncertainties in our understanding of this process* [22].

*….assessment of the consequences of uncertainties in parameter values, are generally much more timeconsuming and challenging than the modelling itself* [23].

Unfortunately, knowledge and data are most limiting in relation to the underlying entomological input parameters that these mathematical models are most sensitive to [24]. While the blood-stage dynamics of malaria parasites in humans are now simulated based on hundreds of observed time courses for individual human infections, and calibrated against tens of thousands of malaria prevalence data sets from the field, epidemiologically important variability in survival demographics between different mosquito populations [25] remains to be captured in commonly-used malaria transmission models. Given the central importance of mosquito survival and gonotrophic cycle duration as targets for many vector control measures [24], it is remarkable that we know little about foraging and mortality processes occurring outside the artificial indoor environment of experimental huts (**Figure 1**). Only a handful of sites exist globally for which estimates of local vector survival, host preference, biting pattern, and adult emergence rates are all available, so that malaria transmission models can be explicitly tailored to the dynamic properties of local vector populations [26]. Indeed, several independently formulated families of models rely heavily on a single village in southern Tanzania for several of their most important vector parameter estimates [4, 15, 27, 28].

insecticidal nets (LLINs) and indoor residual spraying (IRS) for population suppression of mosquitoes which feed or rest indoors, and also extend control of adult mosquitoes outdoors [1–3]. However, the greatest challenge that lies ahead is defining exactly which of these intervention options is necessary and optimal [4] in each of the diverse vector systems that support malaria transmission across the tropics [5–7]. Product developers and manufacturers need a manageably short list of ecologically-defined target product profiles to work with, based on quantitatively characterized traits of wild vector populations [6, 8]. Assuming an adequate arsenal of diverse and mutually-complementary vector control strategies can be made available [2, 3], malaria control programmes will then need to select the most effective subset of these options that they can afford and realistically implement [9], based on longitudinal, nationally-representative surveys of key behav-

As a result of long-term investments in the industrial development pipeline initiated over a decade ago [12], a diversity of new insecticide formulations for malaria vector control products are coming onto the market and entirely new insecticide classes will soon follow [13, 14]. It is also encouraging that a growing diversity of new or repurposed vector control methods are emerging which either use insecticides more efficiently and effectively, or even do entirely without them [2, 3]. Indeed, a range of new vector control technologies are now emerging for tackling a much wider range of mosquito behaviours and species in more diverse tropical

Several detailed models of malaria transmission have been independently developed over the last decade, and integrated into collaborative ensemble platforms [15–21] that have successfully informed global policy [22]. However, as these models develop and improve, further progress is increasingly limited by lack of knowledge rather than global mathematical

*Differences in the predicted impact size arise due to the different assumptions made about malaria transmission in each model, which represent realistic uncertainties in our understanding of this process* [22].

*….assessment of the consequences of uncertainties in parameter values, are generally much more time-*

Unfortunately, knowledge and data are most limiting in relation to the underlying entomological input parameters that these mathematical models are most sensitive to [24]. While the blood-stage dynamics of malaria parasites in humans are now simulated based on hundreds of observed time courses for individual human infections, and calibrated against tens of thousands of malaria prevalence data sets from the field, epidemiologically important variability in survival demographics between different mosquito populations [25] remains to be captured in commonly-used malaria transmission models. Given the

**2. Knowledge and methodology limitations to improved vector** 

ioural and physiological traits [6–11].

404 Towards Malaria Elimination - A Leap Forward

**control products and practices**

*consuming and challenging than the modelling itself* [23].

settings [2, 3].

capacity:

Many of the biggest knowledge gaps relating to malaria vector biology arise from our inability to observe, track or label mosquitoes over large, important parts of their life cycles that occur outdoors. Crucially, the outdoor environment represents a refuge for mosquitoes from currently prioritized indoor-targeted interventions like LLINs and IRS. Important limitations to existing entomological methodology includes: (1) representative sampling of outdoor-resting, blood-fed mosquitoes for surveying host choice, especially beyond the peri-domestic environment; (2) observing, tracing or tracking mosquitoes when they are not host-seeking, especially outdoors; (3) quantifying and mapping participation of males and females in mating swarms; (4) quantifying and mapping of oviposition behaviour and; (5) mapping dispersal between emergence, mating, feeding, resting, and oviposition sites.

**Figure 1.** A schematic illustration of major gaps in knowledge about even the most simplistic conceptual model of a mosquito life cycle that are relevant to interventions targeting human-feeding mosquitoes. For simplicity, some common mosquito life history processes excluded, viz., include feeding on animals, feeding upon sugar, swarming and mating. *Source*: Ref. [29–32].
