FLOODMAL

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Malaria is a vector-borne disease which occurs where Anopheles mosquito vectors, Plasmodium parasites, and vulnerable human populations coincide. There are many opportunities to break the malaria transmission cycle; considerable recent progress has been achieved by protecting human populations using insecticide-treated bednets and house-spraying, and treating the parasite using drug therapies when prevention fails. Nonetheless, these gains are threatened by emerging resistance of mosquitoes to insecticides and drug resistance in the parasite. In addition to protecting human hosts and treating the parasite, however, there is an additional opportunity: control of mosquito vector populations - not only within houses, but in aquatic habitats in the wider environment. In order to target mosquitoes in these larval habitats, we must understand when and where they occur. Whilst habitats can be identified from satellite imagery, this approach can only provide a snapshot of their current distribution; in order to implement interventions such as larviciding or habitat management, we need to know their distribution in advance. Prediction in floodplain environments can only be possible through an understanding of how water bodies are created by flooding, and how these water bodies are used by mosquito vector populations. It is therefore necessary to combine the respective scientific disciplines of hydrology and ecology to gain the process-based understanding needed to predict larval habitat distributions for targeting. Numerous flood modelling techniques have been used for decades to predict the distribution of water across a floodplain, yet this approach has not been used to predict aquatic mosquito habitats. Our project will combine an established flood model with an agent-based model which simulates the feeding, breeding and dispersal of a mosquito population at landscape scale, based on the interactions between these malaria vectors and the distributions of their habitat and hosts. We will integrate these established modelling approaches with innovative use of latest generation earth observation imagery and field data from the Barotse floodplain of the Zambezi River in Zambia's Western Province. We believe that this interdisciplinary approach will allow us to predict the distribution of highly seasonal hotspots of malaria vector larval abundance for the first time at an appropriate scale and extent for implementing interventions. This platform will not only enable us to predict the short-term dynamics of malaria vector hotspots, but also to assess the potential impact of future climate change on malaria transmission hazard between now and the end of the 21st century. Whilst the effect of temperature on malaria vectors has been widely studied, the impacts of changed river flows on malaria hazard - and the intervention efforts which will be required to address them - remain largely unknown. To provide the resilience required by the Zambian government's campaign to eliminate malaria by 2020, this is a knowledge gap which must be filled.