Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour

Malaria hotspots have been the focus of public health managers for several years due to the potential elimination gains that can be obtained from targeting them. The identification of hotspots must be accompanied by the description of the overall network of stable and unstable hotspots of malaria, especially in medium and low transmission settings where malaria elimination is targeted. Targeting hotspots with malaria control interventions has, so far, not produced expected benefits. In this work we have employed a mechanistic-stochastic algorithm to identify clusters of super-spreader houses and their related stable hotspots by accounting for mosquito flight capabilities and the spatial configuration of malaria infections at the house level. Our results show that the number of super-spreading houses and hotspots is dependent on the spatial configuration of the villages. In addition, super-spreaders are also associated to house characteristics such as livestock and family composition. We found that most of the transmission is associated with winds between 6pm and 10pm although later hours are also important. Mixed mosquito flight (downwind and upwind both with random components) were the most likely movements causing the spread of malaria in two out of the three study areas. Finally, our algorithm (named MALSWOTS) provided an estimate of the speed of malaria infection progression from house to house which was around 200-400 meters per day, a figure coherent with mark-release-recapture studies of Anopheles dispersion. Cross validation using an out-of-sample procedure showed accurate identification of hotspots. Our findings provide a significant contribution towards the identification and development of optimal tools for efficient and effective spatio-temporal targeted malaria interventions over potential hotspot areas. Author summary The dispersal of infectious Anopheles mosquitoes is critical to determining the geographical range over which malaria parasites are transmitted between human hosts and mosquito vectors. Malaria rates in the human population vary over space and time and are often characterised by hotspots, where disproportionately few hosts or individuals contribute to malaria transmission. Here, we present an approach to determine the location of malaria hotspots and super spreader houses based on modelling infectious mosquito movements from house to house using infection and wind data collected as part of a wider malaria study in southern Malawi. From our model, we show that it is possible to determine key components of malaria transmission including the identification of stable and unstable malaria hotspots (including super spreader houses), how quickly malaria spreads between households, quantify the importance of village configuration on malaria spread and identify the most important wind types in the local ecological setting. We conclude that it is possible to determine networks of mosquito-borne infection from combining infection and wind data. The identification of malaria hotspots presents an opportunity to target malaria control efforts in areas where malaria is disproportionately high.