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Background The persistence of malaria in large parts of sub-Saharan Africa has motivated the development of novel tools to complement existing control programmes, including gene-drive technologies to modify mosquito vector populations. Here, we use a stochastic simulation model to explore the potential of using a driving-Y chromosome to suppress vector populations in a 106 km2 area of West Africa including all of Burkina Faso. Results The consequence of driving-Y introductions is predicted to vary across the landscape, causing elimination of the target species in some regions and suppression in others. We explore how this variation is determined by environmental conditions, mosquito behaviour, and the properties of the gene-drive. Seasonality is particularly important, and we find population elimination is more likely in regions with mild dry seasons whereas suppression is more likely in regions with strong seasonality. Conclusions Despite the spatial heterogeneity, we suggest that repeated introductions of modified mosquitoes over a few years into a small fraction of human settlements may be sufficient to substantially reduce the overall number of mosquitoes across the entire geographic area. Electronic supplementary material The online version of this article (10.1186/s12915-019-0645-5) contains supplementary material, which is available to authorized users.

Background The use of gene drive systems to manipulate populations of malaria vectors is currently being investigated as a method of malaria control. One potential system uses driving endonuclease genes (DEGs) to spread genes that impose a genetic load. Previously, models have shown that the introduction of DEG-bearing mosquitoes could suppress or even extinguish vector populations in spatially-heterogeneous environments which were constant over time. In this study, a stochastic spatially-explicit model of mosquito ecology is combined with a rainfall model which enables the generation of a variety of daily precipitation patterns. The model is then used to investigate how releases of a DEG that cause a bias in population sex ratios towards males are affected by seasonal or random rainfall patterns. The parameters of the rainfall model are then fitted using data from Bamako, Mali, and Mbita, Kenya, to evaluate release strategies in similar climatic conditions. Results In landscapes with abundant resources and large mosquito populations the spread of a DEG is reliable, irrespective of variability in rainfall. This study thus focuses mainly on landscapes with low density mosquito populations where the spread of a DEG may be sensitive to variation in rainfall. It is found that an introduced DEG will spread into its target population more reliably in wet conditions, yet an established DEG will have more impact in dry conditions. In strongly seasonal environments, it is thus preferable to release DEGs at the onset of a wet season to maximize their spread before the following dry season. If the variability in rainfall has a substantial random component, there is a net increase in the probability that a DEG release will lead to population extinction, due to the increased impact of a DEG which manages to establish in these conditions. For Bamako, where annual rainfall patterns are characterized by a long dry season, it is optimal to release a DEG at the start of the wet season, where the population is growing fastest. By contrast release timing is of lower importance for the less seasonal Mbita. Conclusion This analysis suggests that DEG based methods of malaria vector control can be effective in a wide range of climates. In environments with substantial temporal variation in rainfall, careful timing of releases which accounts for the temporal variation in population density can substantially improve the probability of mosquito suppression or extinction. Electronic supplementary material The online version of this article (10.1186/s12936-018-2259-8) contains supplementary material, which is available to authorized users.

International audience; Background: In many locations malaria is transmitted by more than one vector species. Some vector control interventions, in particular those using genetic approaches, are likely to be targeted against a single species or species complex, at least initially, and it would therefore be useful to be able to predict the epidemiological impact of controlling a single species when multiple vector species are present. Methods: To address this issue, the classical Ross-McDonald model of malaria epidemiology is expanded to account for multiple vector species, giving expressions for the equilibrium prevalence, sporozoite rates and reproductive number. These allow one to predict when control of just one vector species will lead to elimination of the disease. Application of the model is illustrated using published data from a particularly extensive entomological and epidemiological survey before the rollout of bed nets in eastern Kenya, where Anopheles gambiae s.l. and An. funestus were vectors. Results: Meta-analysis indicates that sporozoite rates were 38 % higher in An. gambiae s.l. than in An. funestus, and, according to the model, this difference could be due to An. gambiae s.l. having a higher frequency of feeding on humans, a higher human-to-mosquito transmission rate, a lower adult mortality rate, and/or a shorter incubation period. Further calculations suggest that An. gambiae s.l. would have been sufficient to maintain transmission by itself throughout the region, whereas An. funestus would not have been able to support transmission by itself in Malindi District. Conclusions: Partitioning the contributions of different vector species may allow us to predict whether malaria will persist after targeted vector control.