The Roll Back Malaria (RBM) initiative has published a framework for malaria early warning systems (MEWS) in Africa . These systems rely on indicators of vulnerability, transmission risk and early case detection in order to predict the onset and severity of malaria epidemics. Monitoring rainfall has been recognized as an essential component for MEWS and is being used by malaria control programmes in a number of African countries . Hay et al  retrospectively determined that monitoring dekadal (every 10 days) estimates of rainfall anomalies provided by the Africa Data Dissemination Service (ADDS) could have provided a reliable warning of a major malaria epidemic that occurred in 2002 in Kenya. Thomson et al  suggested that in Botswana, rainfall from December through February could be used to give an early warning for high transmission years.
While excess rainfall is often associated with increased malaria transmission, this is not always the case. For example, heavy rainfall associated with the 1997-98 El Nino event was associated with decreased malaria transmission in the highlands of Tanzania, presumably by washing away larval breeding sites . Similarly, decreases in rainfall have been observed to increase malaria transmission by creating breeding pools in areas where flowing water would normally wash larva away . With a hydrology driven model such as Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), the relationships between anomalous levels of rainfall and malaria transmission can be explicitly represented, allowing the user to draw the correct conclusions from information regarding rainfall patterns.
The development of HYDREMATS is described in detail in Bomblies et al . The model was developed to simulate village-scale response of malaria transmission to interannual climate variability in semi-arid desert fringe environments such as the Sahel. The model provides explicit representation of the spatial determinants of malaria transmission. HYDREMATS can be separated into two components: the hydrology component which explicitly represents pooled water available to anopheles mosquitoes as breeding sites, and the entomology component, which is an agent-based model of disease transmission.
In the hydrology component, rainfall is partitioned between runoff and infiltration, with soil and vegetation properties strongly influencing the partition between these two processes. Uptake of soil water from evapotranspiration is calculated based on climatic variables. Overland flow is modelled using a finite difference solution, and flow velocity is calculated as a function of friction slope, flow depth, and a distributed roughness parameter derived from soil characteristics and vegetation type. The overland flow process is of critical importance for the modelling of water pool formation. The hydrology component of HYDREMATS simulates the spatial distribution of water depths and temperatures for each grid cell, for each timestep. These distributions serve as the inputs for the entomology component of the model .
The entomology component of HYDREMATS simulates individual mosquito and human agents. Human agents are immobile, and are assigned to village residences, as malaria transmission in this region occurs primarily at night when humans are indoors . Mosquito agents have a probabilistic response to their environment based on a prescribed set of rules governing dispersal and discrete events including development of larval stages, feeding, egg-laying and death. The model tracks the location, infective status and reproductive status of each female mosquito through time. Mosquitoes become infected when they bite an infectious human, and after a temperature dependant time lag, can transmit the parasite to humans during subsequent blood meals .
In addition to the water depth inputs supplied by the hydrology component of the model, the entomology component requires air temperature, humidity, wind speed and wind direction. Air temperature and relative humidity influence mosquito behaviour and survival, while wind speed and direction influence mosquito flight, both by physical displacement by wind, and by attracting mosquitoes to upwind blood sources. The location of village residences is required in order to assign the location of human agents . The outputs from HYDREMATS include the number of adult mosquitoes and the vectorial capacity at each time step. Vectorial capacity is a measure of the mosquito's ability to transmit disease, and is defined as the average number of human inoculations of a parasite originating from a single case of malaria, if all vectors biting the original case were to become infected .
There is a natural lag time between rainfall and malaria transmission, as rainfall must first be routed into water pools, and eggs laid in these pools must develop to adulthood before they can begin transmitting the disease. In this study, a simple method was developed to estimate rainfall two and four weeks in advance. The lead time gained by these forecasts combined with the natural lag time allows us to use HYDREMATS to make accurate predictions for the potential for malaria transmission as measured by vectorial capacity several weeks in advance. While this does not have the same advantages of a warning several months in advance, it has less uncertainty than longer range seasonal forecasts, and it could nonetheless be helpful as it would allow malaria control programmes some time to redistribute drug supplies, prepare health clinics for an influx of cases, engage in vector control activities and raise public awareness.