Reduction of arthropod vector populations is the primary tool against many vector-borne diseases (VBDs), from Zika to dengue and Chagas disease. For malaria, the VBD with highest burden worldwide, control of its mosquito vectors has substantially improved health and economic progress over the last decades. However, in the last two years, malaria incidence has unexpectedly increased. It is hypothesized that widespread insecticide and behavioural resistances of major malaria vectors allows them to evade control. New tools are under development but, unless we understand the biological processes that allow vector populations to resist or evade interventions, long-term control will remain elusive. Vector surveillance is continually generating extensive, longitudinal datasets that might enable identification of the factors that underpin population persistence and regulation. However, the demographic analytical tools required to overcome the complexity of these data have rarely been applied in vector ecology to a public health context. Here, we fill this critical gap by combining stage-structured population models implemented under a Bayesian state-space framework with monthly vector surveillance from a 2-year, large-scale randomised clinical trial of a new potentially “resistance busting” bednet (i.e. Olyset-DUO) implemented in Burkina Faso.
Results/Conclusions
Our population modelling approach captured the population dynamics of malaria vectors and allowed us to quantify the demographic impacts of specific interventions on natural mosquito populations. The Olyset-DUO bednets are coated with two chemicals: a pyrethroid, which targets adult mosquito survival, and pyriproxyfen, an insect hormone growth regulator which targets adult fecundity. We found that this intervention reduced vector population density by ~20%. While larval density-dependence played a critical role in balancing adult survival and fecundity trade-offs in perturbed wild populations, the Olyset DUO bednets was found to impact both these demographic parameters. Quantification of the changes in key mosquito life-history parameters triggered in response to interventions will aid the development of optimal control strategies that drive populations to collapse. More broadly, bridging contemporary ecological modelling with public health data on vector-borne diseases can provide game-changing insights into the factors that underpin the persistence of vector populations, allowing more effective targeting of interventions. This is particularly important as we move from control to elimination of VBDs, for which malaria control is at the forefront.