A multi-criteria decision analysis approach to assessing malaria risk in northern South America.

BACKGROUND: Malaria control in South America has vastly improved in the past decade, leading to a decrease in the malaria burden. Despite the progress, large parts of the continent continue to be at risk of malaria transmission, especially in northern South America. The objectives of this study were to assess the risk of malaria transmission and vector exposure in northern South America using multi-criteria decision analysis. METHODS: The risk of malaria transmission and vector exposure in northern South America was assessed using multi-criteria decision analysis, in which expert opinions were taken on the key environmental and population risk factors. RESULTS: Results from our risk maps indicated areas of moderate-to-high risk along rivers in the Amazon basin, along the coasts of the Guianas, the Pacific coast of Colombia and northern Colombia, in parts of Peru and Bolivia and within the Brazilian Amazon. When validated with occurrence records for malaria, An. darlingi, An. albimanus and An. nuneztovari s.l., t-test results indicated that risk scores at occurrence locations were significantly higher (p < 0.0001) than a control group of geographically random points. CONCLUSION: In this study, we produced risk maps based on expert opinion on the spatial representation of risk of potential vector exposure and malaria transmission. The findings provide information to the public health decision maker/policy makers to give additional attention to the spatial planning of effective vector control measures. Therefore, as the region tackles the challenge of malaria elimination, prioritizing areas for interventions by using spatially accurate, high-resolution (1 km or less) risk maps may guide targeted control and help reduce the disease burden in the region.

Prospects and recommendations for risk mapping to improve strategies for effective malaria vector control interventions in Latin America.

With malaria control in Latin America firmly established in most countries and a growing number of these countries in the pre-elimination phase, malaria elimination appears feasible. A review of the literature indicates that malaria elimination in this region will be difficult without locally tailored strategies for vector control, which depend on more research on vector ecology, genetics and behavioural responses to environmental changes, such as those caused by land cover alterations, and human population movements. An essential way to bridge the knowledge gap and improve vector control is through risk mapping. Malaria risk maps based on statistical and knowledge-based modelling can elucidate the links between environmental factors and malaria vectors, explain interactions between environmental changes and vector dynamics, and provide a heuristic to demonstrate how the environment shapes malaria transmission. To increase the utility of risk mapping in guiding vector control activities, definitions of malaria risk for mapping purposes must be standardized. The maps must also possess appropriate scale and resolution in order to become essential tools in integrated vector management (IVM), so that planners can target areas in greatest need of control measures. Fully integrating risk mapping into vector control programmes will make interventions more evidence-based, making malaria elimination more attainable.

Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population.

BACKGROUND: Changes in land use and land cover (LULC) as well as climate are likely to affect the geographic distribution of malaria vectors and parasites in the coming decades. At present, malaria transmission is concentrated mainly in the Amazon basin where extensive agriculture, mining, and logging activities have resulted in changes to local and regional hydrology, massive loss of forest cover, and increased contact between malaria vectors and hosts. METHODS: Employing presence-only records, bioclimatic, topographic, hydrologic, LULC and human population data, we modeled the distribution of malaria and two of its dominant vectors, Anopheles darlingi, and Anopheles nuneztovari s.l. in northern South America using the species distribution modeling platform Maxent. RESULTS: Results from our land change modeling indicate that about 70,000 km(2) of forest land would be lost by 2050 and 78,000 km(2) by 2070 compared to 2010. The Maxent model predicted zones of relatively high habitat suitability for malaria and the vectors mainly within the Amazon and along coastlines. While areas with malaria are expected to decrease in line with current downward trends, both vectors are predicted to experience range expansions in the future. Elevation, annual precipitation and temperature were influential in all models both current and future. Human population mostly affected An. darlingi distribution while LULC changes influenced An. nuneztovari s.l. distribution. CONCLUSION: As the region tackles the challenge of malaria elimination, investigations such as this could be useful for planning and management purposes and aid in predicting and addressing potential impediments to elimination.