Following discussion among members of the RBM-TSN a consensus map of epidemic risk zones was produced [7]. The map, shown in Figure 1, was used as a mask to exclude areas where malaria transmission is considered absent or endemic, as opposed to epidemic. This mask is based purely on climatic constraints to malaria transmission, and does not yet account for areas in the northern and southern margins of the continent where control has eliminated malaria risk.
The epidemic risk map was then combined with rainfall anomaly data (i.e., the difference between observed rainfall and the expected, i.e. average, rainfall for a particular time of the year) to provide a simple indicator of changes in risk in epidemic prone areas. Figure 2 illustrates the resulting dekadal (i.e., ~10-daily) rainfall anomaly maps, which are updated approximately every 10 days, and have been available in experimental form through the Africa Data Dissemination Service (ADDS) since June 2002. The ADDS is an operational part of the Famine Early Warning Systems Network (FEWS NET) which is maintained by the United States Geological Survey (USGS) and supported by the U. S. Agency for International Development (USAID). The maps can be accessed at: http://igskmncnwb015.cr.usgs.gov/adds/ under "RFE Anomaly – Malaria", where the anomaly map for the most recent dekad is displayed along with documentation on how the map is produced. The existence of this online monitoring resource was publicized and their use and validation by control services and researchers was encouraged [8]. The rainfall estimate data underlying these maps was tested against laboratory-confirmed malaria incidence figures for selected districts in Southern Africa, where they showed a good association [9].
In the year following the launch of the ADDS dekadal rainfall anomaly maps, WHO commissioned field visits to a number of epidemic prone countries to evaluate whether the National Malaria Control Programmes were aware of this resource and how useful they considered it may be for their efforts. Sudan, Uganda, Niger, Mali and Burkina Faso received field visits. In general, all of the control programmes had been aware of the rainfall anomaly maps, but only those in Uganda and Sudan had monitored them regularly during the previous year. The control programmes in the Sahelian countries did not agree with the epidemic risk zone used in the mask because their recent experience was that epidemic outbreaks had occurred beyond the northern boundary of the epidemic risk zone. This was also partly true in Sudan where epidemic outbreaks have been known to occur along the Nile River margins in the northern half of the country. Uganda's malaria control programme, however, had found the maps to be reasonably accurate and a useful monitoring resource. Further dialogue with malaria control programmes in West Africa and Southern Africa also raised the point that a single dekadal rainfall anomaly map could raise an alert; when in fact the rainfall levels were not abnormally high – but just 10 days earlier than 'normal'. This suggested that additional information about the temporal distribution of rainfall was necessary.
In order to respond to these issues, USGS and WHO-HealthMapper agreed to collaborate on the development of the dekadal anomaly maps in a format which could be downloaded, viewed and archived by surveillance staff directly in HealthMapper, a basic mapping and surveillance software developed by WHO's Communicable Disease Surveillance and Response Department http://www.who.int/csr/mapping/tools/healthmapper/healthmapper/en/. In addition to the most recent map, it is possible to download the dekadal maps for the previous six months and begin to construct a seasonal time series. The integration of the rainfall anomalies maps within HealthMapper also allowed the users to improve their analyses by combining ancillary data related to malaria directly on top of the rainfall anomalies maps for their country. Figure 3 provides an example for Niger.
Staff working at the International Research Institute for Climate Prediction (IRI) have since developed a web-based Malaria Early Warning System (MEWS) interface that enables the user to gain a broader contextual perspective of the current rainfall season by comparing it to previous seasons and climatological averages. The interface is in the IRI Data Library and takes the form of an online 'clickable map' of Africa: http://iridl.ldeo.columbia.edu/maproom/.Regional/.Africa/.MEWS/. It displays the most recent dekadal rainfall map (Figure 4) over which national and district administrative boundaries and the epidemic risk zone can be overlaid (in this case as a guide rather than an absolute mask which may have excluded districts of local interest). These visual features can be toggled on or off and the user can zoom in to any region for more clarity. The user can 'zoom' into a more localized region of interest. Dekadal rainfall can be spatially-averaged over a variety of user-selected areas, including administrative districts and 11 × 11 km, 33 × 33 km, 55 × 55 km and 111 × 111 km boxes. Upon the selection of this sampling area and a specific location of interest (by a click on map at the location of interest), four time-series graphs are generated (Figure 5). These time-series provide an analysis of recent rainfall with respect to that of recent seasons and the overall climatology. A description of the time-series figures, the data used and its source is also provided.