Still Waters: using hydrological models to improve malaria transmission estimates

The decades-long fight against human malaria is entering a new phase, one that the WHO advocates will require targeted interventions tuned to the way the disease is transmitted locally, particularly in sub-Saharan Africa where most infections still occur. Malaria is a climate-sensitive vector-borne disease; the environment plays a key role, especially via the distribution and timing of water suitable for mosquito breeding in relation to where people live. But this relationship is complex and largely omitted from planning by Ministries of Health. This is a key gap in the face of challenges to malaria control in some habitats and from climate change. We aim to solve this globally important problem using the latest advances in climate science, hydrological modelling, and satellite remote sensing to produce new surface water maps that can be readily incorporated into malaria transmission models and combined with routine clinical data. To do so we will need to ‘push the frontiers’ of the science and work across disciplines, but this offers the potential for a new generation of environmental risk mapping for bespoke village-scale malaria control over large areas, as required by public health planners. This will be an important scientific contribution to achieve the mid-century goal of malaria elimination.
 
While national strategic plans recognise the critical importance of water for malaria transmission, water is represented in extremely basic ways such as by rainfall totals or distance to a river. We argue that more advanced hydrological methods should be incorporated in such strategies. Surface water availability is a key control on the malaria transmission cycle, as water bodies are the larval habitat for the Anopheles mosquito vectors that transmit the malaria parasite. Without surface water, there would be no malaria. However, malaria models typically use only rainfall to estimate malaria transmission suitability and bypass hydrology altogether. Infiltration, evaporation and water flow through rivers are important controls on surface water. By embedding hydrology within malaria transmission models, we can improve process understanding and identify dynamic patterns of malaria transmission, assisting in the targeting of malaria interventions, for example by identifying larval habitats for optimised larviciding campaigns or predicting the changing malaria burden in response to El Niño or climatic changes.
 
Working with our established collaborations at the Ministry of Health in Zambia, our interdisciplinary team is uniquely placed to deliver this important advance. Specifically, we will drive a new generation of surface water models with rainfall from high-resolution climate models. We will test our model estimates of present-day surface water against new satellite surface water mapping methods that can now correctly identify submerged vegetation. Potential Anopheles breeding habitats derived from these water maps will be input into an existing malaria transmission model to estimate monthly transmission rates. We will also test ‘lighter-touch’ approaches using less computationally intense methods that can be more readily included in malaria control strategies. Finally, we will validate model outputs with malaria incidence data from hundreds of health facilities across Zambia.
 
The risk mapping arising from this work is designed to be readily incorporated into health information systems used widely in Africa and LMICs globally and will inform malaria control strategies beyond Zambia. It will provide robust process-based estimates of both the environmental controls on present day malaria burden and how these might change in future, with impact directed at national malaria elimination strategies.