Background
Malaria continues to exact a toll in many developing countries where it is endemic through the economic and health burden it imposes. Malaria has historically contributed to increased health costs, decreased productivity, and slow rates of economic growth in 80 developing countries [
1]. An estimation in 2013 showed that about 198 million cases and 584,000 deaths related to malaria occurred globally [
2]. Although sub-Saharan Africa bears a disproportionately larger burden of the disease, South America also bears a significant case burden, with approximately 427,000 confirmed cases and 82 deaths in 2013 [
2]. Of these, the nine countries in northern South America (NSA) accounted for ~ 90 % of the malaria cases in the continent [
3]. Despite these figures, there have been vast improvements in malaria control in the past decade [
2], so much so that malaria elimination in the NSA now seems feasible in the foreseeable future.
Global efforts to eliminate malaria such as the Roll Back Malaria program aim to “shrink the malaria map by progressively eliminating malaria from endemic margins inward” [
4]. Achieving malaria elimination in the NSA, as in other region, will involve the systematic and synergistic use of multiple strategies including targeting areas for malaria interventions based on a stratification of risk. Spatially accurate, high-resolution risk maps delimiting areas of likely human-vector contact would not only help prioritize areas for malaria intervention, but also aid monitoring and evaluation of such interventions [
5].
The stratification of risk depends on how risk is defined, yet there is currently no standard definition. Risk definitions have been dependent on the subject matter or purpose of the investigation [
6]. Risk is broadly defined in public health as “the probability of disease developing in an individual in a specified time interval” [
7]. Malaria risk is however not clearly defined due to the complexity of the disease that involves multiple hosts, vectors, and pathogens. Malaria risk has been defined using human cases (e.g. incidence and prevalence [
8]), probability of
Plasmodium presence [
9], intensity of transmission [
10], or its vectors (e.g. vector exposure [
5], vector presence [
11], and habitat suitability of vectors [
12]). Thus, malaria risk is broadly considered as an array of factors that relate to the presence and density of vectors and parasites, all of which vary in space and time.
The direct estimation of malaria risk often involves malaria diagnosis and its relationship to populations at risk [
13], but periodic, field-based survey data are typically limited in space and time in developing countries. Alternatively, in areas with limited data, malaria risk may be estimated indirectly through environmental covariates, which often show strong associations with malaria and mosquito distributions. The combination of these environmental surrogates in geographic information system (GIS) decision-support algorithms can reveal unexpected spatial patterns of malaria risk at unprecedented spatial resolutions [
5]. Many types of spatial data derived from remotely sensed observations such as digital elevation models from the Shuttle Radar Topography Mission (SRTM) are now publicly available for most parts of the world, thus facilitating the potential estimation of malaria risk across large areas across multiple political units [
5].
One method of mapping disease risk with limited field-based epidemiological or vector data is multi-criteria decision analysis (MCDA). This approach is preferred for its participatory framework, which employs statistical methods and human intuition, allows expert interaction, and accommodates non-linear relationships common between disease organisms and the environment [
14,
15]. MCDA allows the combination of multiple environmental factors in estimating disease risk by employing decision rules derived from existing knowledge or hypothesized understanding of the causal relationships leading to disease occurrence [
5,
15]. The output is a composite map which indicates lower or higher potential of disease occurrence in a location relative to surrounding areas on the same map [
16]. MCDA has been useful in assessing risk of vector-borne diseases such as predicting suitable areas for rift valley fever in Africa [
17], prioritizing areas of tsetse fly control in Zambia [
18], malaria vector control in Madagascar [
19] and risk of malaria vector exposure in parts of South America [
5]. Building on the work by Fuller et al. [
5], we set out in this study to evaluate malaria risk in the NSA based on environmental factors to produce risk maps that could guide targeted malaria interventions and potentially accelerate the drive towards malaria elimination in the region.
Conclusion
We evaluated the exposure of the NSA to malaria risk given current access-related and environmental/climatic conditions using MCDA. We produced high-resolution composite maps showing gradients of risk which were validated with geo-coded occurrence points for malaria and three dominant vector species. These new map products represent an improvement upon previously published map of malaria risk in the region, which was highly generalized and constrained by political boundaries [
50,
53]. The incorporation of a deforestation layer representing land-use change, provided additional detail to the risk maps relative to past studies that have employed MCDA for malaria vector exposure risk [
5]. This also revealed that our depiction of risk produced was related to malaria occurrence points. Despite limitations of the knowledge-based approach to risk mapping, our 1 km maps provide information to the public health decision makers/policy makers to give additional attention to the spatial planning of effective vector control measures. This may increase the potential for malaria elimination in the region in the near future.
Ethics approval
Ethical clearance was not sought because human subjects were not involved.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
TOA and DOF conceptualized the idea for the manuscript and wrote the draft. TOA conducted the analysis; SVH, MAH, MLQ, JBS and JCB critically revised the intellectual content of the manuscript. All authors read and approved the final manuscript.