Malaria stratification is a classification of areas according to the risk of malaria. It is a way to set priorities and target prevention efforts to the areas where they are most needed. It can highlight areas where the control program needs extra effort, and helps to make the best use of resources.

Since control activities are mostly organized by an administrative unit such as a subzoba , a classification of these units is more useful to the program than a strict ecological stratification, where different environmental strata may overlap more than one subzoba . Risk stratification can be extended to smaller units within subzobas , if desired. The cluster classification provides guidance for targeting extra malaria control methods, such as indoor residual spraying, to the highest incidence areas. It also clarifies the seasonality differences between regions and indicates optimum timing of interventions.

Given that many malarious countries have established or improved their health management information systems as repositories for surveillance and monitoring data, the development of a stratification method that depends on such data has great potential.

The dataset used for stratification also provides necessary outcome data for assessing the effectiveness of different control methods used in the country over the last 8 years. However, since malaria anomalies and rainfall/NDVI are clearly associated in Eritrea, it is imperative that an analysis of the impact of interventions takes into account climate/environment variability.

The climate/environmental variables used in this report can be routinely monitored and in fact, some are already used within Eritrea for Food Security and Locust monitoring. These environmental variables have the potential to be partially predicted using seasonal climate forecasts, although this predictability is unlikely to be as good as other parts of east and southern Africa.

There is a known trade-off between timing and accuracy in any such warning system. In our example:

1. Health surveillance using epidemic thresholds detects the early phase of epidemics and provides a couple of weeks warning of epidemic peak.
2. Environmental monitoring of NDVI provides concurrent prediction of malaria anomalies.
3. Rainfall from gauge data provides prediction of malaria anomalies with a lead time of 2-3 months for some subzobas with meteorological stations.
4. Rainfall from merged gauge and satellite data (CMAP) predicts September or January NDVI, when peak malaria occurs, with 2-3 months lead time.
5. General circulation models predict rainfall with a lead-time of 2 months and NDVI with a lead-time of 4 months, but with reasonable skill only in El Niño years for most parts of Eritrea.

Stratification and definition of the relationship between climate and malaria are significant steps towards the development of an early warning system. Once a picture of the ‘expected’ number of monthly (or perhaps in the future, weekly) malaria cases in a subzoba or cluster has been built up, together with the expected climate variables in the same areas, all the necessary components of an early warning system are in place.