BackgroundMalaria control remains a significant challenge in the Solomon Islands. Despite progress made by local malaria control agencies over the past decade, case rates remain high in some areas of the country. Studies from around the world have confirmed important links between climate and malaria transmission. This study focuses on understanding the links between malaria and climate in Guadalcanal, Solomon Islands, with a view towards developing a climate-based monitoring and early warning for periods of enhanced malaria transmission.MethodsClimate records were sourced from the Solomon Islands meteorological service (SIMS) and historical malaria case records were sourced from the National Vector-Borne Disease Control Programme (NVBDCP). A declining trend in malaria cases over the last decade associated with improved malaria control was adjusted for. A stepwise regression was performed between climate variables and climate-associated malaria transmission (CMT) at different lag intervals to determine where significant relationships existed. The suitability of these results for use in a three-tiered categorical warning system was then assessed using a Mann–Whitney U test.ResultsOf the climate variables considered, only rainfall had a consistently significant relationship with malaria in North Guadalcanal. Optimal lag intervals were determined for prediction using R2 skill scores. A highly significant negative correlation (R = − 0.86, R2 = 0.74, p < 0.05, n = 14) was found between October and December rainfall at Honiara and CMT in northern Guadalcanal for the subsequent January–June. This indicates that drier October–December periods are followed by higher malaria transmission periods in January–June. Cross-validation emphasized the suitability of this relationship for forecasting purposes   as did Mann–Whitney U test results showing that rainfall below or above specific thresholds was significantly associated with above or below normal malaria transmission, respectively.ConclusionThis study demonstrated that rainfall provides the best predictor of malaria transmission in North Guadalcanal. This relationship is thought to be underpinned by the unique hydrological conditions in northern Guadalcanal which allow sandbars to form across the mouths of estuaries which act to develop or increase stagnant brackish marshes in low rainfall periods. These are ideal habitats for the main mosquito vector, Anopheles farauti. High rainfall accumulations result in the flushing of these habitats, reducing their viability. The results of this study are now being used as the basis of a malaria early warning system which has been jointly implemented by the SIMS, NVBDCP and the Australian Bureau of Meteorology.

BackgroundMalaria 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.MethodsA 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.ResultsKey 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.ConclusionsThe 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 B...

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