Changing Patterns of Malaria in Grande Comore after a Drastic Decline: Importance of Fine-Scale Spatial Analysis to Inform Future Control Actions

Abstract

Malaria has long been endemic in the Union of Comoros reaching an incidence of 15,045 cases for 100,000 inhabitants in 2010 (103,670 cases). Since then, strengthened control actions based on the distribution of Long-Lasting Insecticidal mosquito Nets and mass treatment have reduced malaria to a low level. However, it persists more specifically in Grande Comore, where 82% of cases were diagnosed between 2010 and 2016. This situation remains a challenge for health authorities seeking to eliminate malaria, by targeting transmission sites more precisely. In this context, this study aimed at mapping malaria at the finest scale, in order to describe its spatial distribution and identify possible environmental indicators. The National Malaria Control Program provided the 2016 data, the only year that could be mapped at the level of localities. This mapping revealed spatial autocorrelation between localities, especially in the east of the island with a major cluster around Itsinkoudi (using the Kulldorff’s spatial scan test). Secondary clusters showed that malaria remains present throughout the island in both rural and urban areas. We also analyzed satellite images (SPOT 5) with remote sensing techniques (Object-Based Image Analysis) to look for environmental indicators. Landscape analysis shows that malaria incidence is correlated across the island with low altitudes, and a larger proportion of grasslands or a fewer proportion of forested areas nearby (at less than 1km around villages). More locally in the east, malaria is linked to larger shrub areas. These relationships could be associated with the fact that lower altitude localities are more interconnected, such facilitating malaria transmission. In 2016, malaria persists in Grande Comore, showing new patterns with more cases in the eastern part of the island and the possibility of high incidences during the dry season. Precise mapping of epidemiological data and landscape analysis allow the identification of clusters and active transmission foci. They are important tools for health surveillance in order to optimize control actions on key transmission locations.