Mapping a Plasmodium transmission spatial suitability index in Solomon Islands: a malaria monitoring and control tool.

Background: Malaria remains a challenge in Solomon Islands, despite government efforts to implement a coordinated control programme. This programme resulted in a dramatic decrease in the number of cases and mortality however, malaria incidence remains high in the three most populated provinces. Anopheles farauti is the primary malaria vector and a better understanding of the spatial patterns parasite transmission is required in order to implement effective control measures. Previous entomological studies provide information on the ecological preferences of An. farauti but this information has never before been gathered and "translated" in useful tools as maps that provide information at both the national level and at the scale of villages, thus enabling local targeted control measures.

Methods: A literature review and consultation with entomology experts were used to determine and select environmental preferences of An. farauti. Remote sensing images were processed to translate these preferences into geolocated information to allow them to be used as the basis for a Transmission Suitability Index (TSI). Validation was developed from independent previous entomological studies with georeferenced locations of An. farauti. Then, TSI was autoscaled to ten classes for mapping.

Results: Key environmental preferences for the An. farauti were: distance to coastline, elevation, and availability of water sources. Based on these variables, a model was developed to provide a TSI. This TSI was developed using GIS and remote sensing image processing, resulting in maps and GIS raster layer for all the eight provinces and Honiara City at a 250 m spatial resolution. For a TSI ranging from 0 as not suitable to 13 as most suitable, all the previous collections of An. farauti had mean TSI value between 9 and 11 and were significantly higher than where the vector was searched for and absent. Resulting maps were provided after autoscaling the TSI into ten classes from 0 to 9 for visual clarity.

Conclusions: The TSI model developed here provides useful predictions of likely malaria transmission larval sources based on the environmental preferences of the mosquito, An. farauti. These predictions can provide sufficient lead-time for agencies to target malaria prevention and control measures and can assist with effective deployment of limited resources. As the model is built on the known environmental preferences of An. farauti, the model should be completed and updated as soon as new information is available. Because the model did not include any other malaria transmission factors such as care availability, diagnostic time, treatment, prevention, and entomological parameters other than the ecological preferences neither, our suitability mapping represents the upper bound of transmission areas. The results of this study can now being used as the basis of a malaria monitoring system which has been jointly implemented by the Solomon Islands National Vector Borne Disease Control Programme, the Solomon Islands Meteorological Services and the Australian Bureau of Meteorology. The TSI model development method can be applied to other regions of the world where this mosquito occurs and could be adapted for other species.