Spatial prediction of Plasmodium falciparum prevalence in Somalia
 Abdisalan M Noor^{1, 2}Email author,
 Archie CA Clements^{3},
 Peter W Gething^{4},
 Grainne Moloney^{5},
 Mohammed Borle^{5},
 Tanya Shewchuk^{6},
 Simon I Hay^{1, 7} and
 Robert W Snow^{1, 2}
DOI: 10.1186/147528757159
© Noor et al; licensee BioMed Central Ltd. 2008
Received: 12 June 2008
Accepted: 21 August 2008
Published: 21 August 2008
Abstract
Background
Maps of malaria distribution are vital for optimal allocation of resources for antimalarial activities. There is a lack of reliable contemporary malaria maps in endemic countries in subSaharan Africa. This problem is particularly acute in low malaria transmission countries such as those located in the horn of Africa.
Methods
Data from a national malaria cluster sample survey in 2005 and routine cluster surveys in 2007 were assembled for Somalia. Rapid diagnostic tests were used to examine the presence of Plasmodium falciparum parasites in fingerprick blood samples obtained from individuals across all agegroups. Bayesian geostatistical models, with environmental and survey covariates, were used to predict continuous maps of malaria prevalence across Somalia and to define the uncertainty associated with the predictions.
Results
For analyses the country was divided into north and south. In the north, the month of survey, distance to water, precipitation and temperature had no significant association with P. falciparum prevalence when spatial correlation was taken into account. In contrast, all the covariates, except distance to water, were significantly associated with parasite prevalence in the south. The inclusion of covariates improved model fit for the south but not for the north. Model precision was highest in the south. The majority of the country had a predicted prevalence of < 5%; areas with ≥ 5% prevalence were predominantly in the south.
Conclusion
The maps showed that malaria transmission in Somalia varied from hypo to mesoendemic. However, even after including the selected covariates in the model, there still remained a considerable amount of unexplained spatial variation in parasite prevalence, indicating effects of other factors not captured in the study. Nonetheless the maps presented here provide the best contemporary information on malaria prevalence in Somalia.
Background
Maps of disease distribution are an essential tool for optimizing the allocation of resources for malaria interventions [1, 2]. There have been a number of attempts to develop malaria transmission maps at different geographic scales based on expert opinion [3, 4]; deterministic biological models driven by the conceptual relationship between transmission and environmental covariates [5]; and empirical transmission models based on entomological inoculation rates [6, 7] or human infection prevalence data [8–17]. These methods suffer several limitations: expert opinion maps are subjective; deterministic models ignore the secular effects of expanded coverage of interventions that supersede the influence of climate on the epidemiology of malaria and do not quantify uncertainty around model results. Where studies have used observational data to predict malaria distributions, most have used historical data collected opportunistically from secondary sources [10, 15, 16] that did not involve random sampling and/or a sampling framework optimized for spatial analysis.
Arguably the greatest need for malaria maps is at the periphery of stable, endemic areas where decisions about the delivery of standard suites of interventions, such as those promoted by the Roll Back Malaria (RBM) initiative to support malaria control in high transmission areas, may become less appropriate or costefficient. In areas of perceived low malaria risk there is little empirical information on the risks and intensity of transmission. As such the semiarid regions of the horn of Africa remain less well described epidemiologically compared to the rest of malaria endemic subSaharan Africa (SSA) and there are no contemporary national maps of the extents of malaria risk. The Malaria Atlas Project (MAP) while maintaining a global remit in its efforts to improve the cartography of malaria [2] is equally committed to developing national mapping initiatives with country partners, where the data available can support rigorous cartography. Somalia represents the first such example.
A Plasmodium falciparum malaria prevalence map for Somalia is presented here using Bayesian geostatistical analysis of communitybased parasite prevalence survey data. The data used in this analysis have several unique features that minimize some of the problems of using retrospectively assembled data: first the community data were derived from random sample surveys undertaken as part of national malaria or nutritional surveys; second all the data were collected using similar methodologies; and finally all the data represent contemporary infection prevalence between 2005 and 2007.
Methods
Country context
The earliest malariometric surveys undertaken in Somalia were in the NorthWest in 1946 which reported a highly varying prevalence distribution of P. falciparum ranging from 0 to 17% across three clusters of villages [20]. Between the 1940s and 2005 there were only three malaria infection surveys across five villages in the Lower Shabelle area of the southcentral zone [21–23]. Based on limited entomological data, malaria transmission is thought to be supported almost entirely by Anopheles arabiensis [24–26].
Assembling the survey data on parasite prevalence
Following the dearth of P. falciparum parasite rate (Pf PR) surveys over the last 50 years, communitybased surveys of Pf PR have become a routine undertaking across the country since 2005. These surveys have been embedded in two major activities. First, a national malaria indicator survey was conducted by the WHO between January and February 2005 in the southcentral and northwest zones [27] and in July 2005 in the northeast [28]. A stratified multistage random sampling strategy was adopted. Within each zone all regions were sampled and out of 120 districts in these regions, 88 were selected at random. Randomly selected villages within each district were surveyed successively until the required number of respondents of all ages (at least 845 per region) was achieved. Second, the United Nations Food Agricultural OrganizationFood Security Analysis Unit (FAO/FSAU) completed 18 independent cluster sample surveys between March and November 2007. Malaria parasitology in all agegroups was included in these routine nutritional surveys at the request of UNICEF. In each survey a stratified multistage cluster sampling design was adopted where the sampling frame of a selected district was based on three livelihood definitions (pastoral, agropastoral, and riverine) [29, 30] within which 30 rural communities and 30 households within each community were selected at random.
In all surveys, evidence of parasitaemia was determined using P. falciparum specific Rapid Diagnostic Tests (RDTs). WHO used ParaHIT – f™ Device (Span Diagnostics Limited, Surat, India) while FSAU used Paracheck Pf™ (Orchid Biomedical Systems, Goa, India). The purpose of the survey was explained to each household head or adult representative from whom informed consent was then sought prior to undertaking parasitological tests. All individuals who tested positive for infection were treated with nationally recommended firstline therapy [31]. An inclusion criterion of a minimum of 40 individuals per community was used to select villages to include in the analysis to minimize random variation inherent in small samples [32, 33].
Survey data from all three sources were combined into a single database. Where communities had been surveyed more than once, the survey with the largest sample size was selected. A detailed search was undertaken to establish a set of spatial coordinates for each community. For some of the later surveys undertaken by FSAU, global positioning systems (GPS) were used to provide a longitude and latitude. For the remaining settlements a combination of electronic gazetteers [34, 35] and other nationally derived UN sources of longitude and latitude [36] were used to locate the community. Finally, the location of each settlement was verified by using Google Earth (Google, Seattle, USA) to visually inspect whether the coordinates matched evidence of human settlement. Those settlements for which no reliable source of the coordinates could be obtained were excluded from the analysis.
Outlier detection
Geostatistical methods are particularly sensitive to outlying values that exert a significant effect on predictions. Extreme outliers were therefore identified and excluded using a spatial filter. The method assumes that the probability of an unusually large Pf PR value being a genuine 'outlier' is larger if (a) it is in a neighbourhood of generally much smaller values and/or (b) the neighbourhood is generally uniform. A spatial filtering algorithm was implemented that incorporated these heuristic considerations (Additional File 1).
Selection of covariates
Climatic and survey covariates were considered for inclusion in the spatial prediction model. The following four climatic variables were considered, each of which was resampled to 5 × 5 km resolution to be consistent with the prediction grid. 1) The enhanced vegetation index (EVI) derived from the global Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for the period 2001–2005 [37]. Temporal Fourier analysis was undertaken to derive a global EVI index [38]. These data were available for each month at 1 × 1 km spatial resolution and obtained from a global archive developed recently by the Spatial Ecology and Epidemiology Group of the Department of Zoology, University of Oxford [38]. Scaled EVI values ranged from 0–1, representing no, to complete vegetation cover. 2) Precipitation and temperature data described as the average monthly precipitation and temperature (minimum and maximum) at 1 × 1 km spatial resolution were downloaded from the WorldClim website [39]. These climate surfaces were developed through interpolation of global meteorological data collected from 1950–2000 [40]. 3) Distance to permanent water bodies was derived for each survey location using information provided by Africover [41] and those of marshes, flood plains and intermittent wetland from the Global Lakes and Wetlands databases [42]. 4) The effect of month of survey was assessed because of the observed temporal heterogeneity of Pf PR data. February was selected as the "reference month" for both zones as this was the earliest calendar month in which surveys were undertaken.
The annual mean of each environmental covariate (derived from the monthly data) was extracted at each survey location using ArcGIS 9.1 (ESRI Inc., USA). To assess the effects of the covariates on observed PfPR, nonspatial binomial logistic regression models were implemented in Stata/SE Version 10 (Stata Corporation, College Station, TX, USA). With PfPR as the dependent variable, bivariate binomial logistic regression models were fitted and covariates with Wald's P > 0.2 were excluded from subsequent analyses. Collinearity among all remaining covariates was assessed and if a pair had a correlation coefficient > 0.9, the variable with the highest value of Akaike Information Criterion (AIC) was discarded. To select which of the two temperature variables (maximum and minimum) to include into the multivariate model, the one with lowest value of AIC was chosen [9]. A nonspatial binomial multivariate logistic regression was then fitted, starting with a saturated model, and then seeking a parsimonious model using backwards variable elimination with an exit criterion of Wald's P > 0.2. Variables that exhibited nonlinear relationships with PfPR were dichotomized at the median.
Bayesian geostatistical models
Bayesian geostatistical (kriging) techniques provide a framework for predicting (interpolating) values of a variable of interest at unobserved locations given a set of spatially distributed data, incorporating spatial autocorrelation and computing uncertainty measures around model predictions [43, 44]. Spatial autocorrelation in the Somalia Pf PR data was therefore first evaluated by computing empirical variograms, a graphical summary of spatial autocorrelation structure, separately for the south and the north. Different variogram structures were observed for the two zones indicating that a single stationary model for the whole country was inappropriate. Comparison of the variograms suggested greater heterogeneity of observed parasite prevalence data in the south than in the north consistent with expert opinion of the transmission dynamics across the country [26]. Consequently models were constructed separately for each zone. Bayesian binomial generalized linear geostatistical models [43] were implemented in each zone with the spatial component modelled as a stationary Gaussian process with mean of zero and covariance structure defined by a powered exponential function [45]. Because survey data were modelled as a conditionally binomial variable, given the underlying Gaussian process, the variance due to sample size was accounted for implicitly. The models were implemented in WinBUGS Version 1.4 (MRC Biostatistics Unit, Cambridge, UK). The models were constructed with and without the covariates in order to compare differences in model fit. Model fit was based on the deviance information criterion (DIC). Models with a smaller DIC value (with a difference >5) were considered to represent a better compromise between parsimony and fit [46]. The rate of decay of correlation between points (ϕ) with distance and the variance of the spatial process (σ^{2}) were also recorded. The form of these models is shown in Additional File 2.
Predictions at nonsampled locations (defined over a regular 5 × 5 km grid overlaying the entire country) were made using the spatial.unipred function in WinBUGS which solves the model equation at each prediction location given the values of each covariate at the prediction location and the distance between prediction locations and observed data locations. Coefficients and model diagnostics were estimated using Markov Chain Monte Carlo (MCMC) simulations. The posterior probability distributions were used to classify prediction points to an endemicity class. Probability of class membership was computed as an additional measure to identify areas of high model uncertainties. For presentation purposes prediction maps from the bestfit model were combined for both zones. Continuous and categorical representations of predicted prevalence and probability maps were produced. The categorical classes of Pf PR selected were 0–<5% (low risk); 5–39% (medium risk); and ≥40% (high risk) and are based on a review of endemicity classification that would be most suitable as a guides for the likely impact of existing interventions [47].
Model validation
A spatially declustered random sampling strategy was implemented to generate validation sets that could be considered spatially representative of the prediction space. Thiessen polygons, which enclose the area closest to a given point, were defined around each survey location. A 10% sample or a minimum of 30 survey locations (whichever was larger) were then drawn randomly for the north and the south with each data point having a probability of selection proportional to the area of its Thiessen Polygon. This meant data located in densely surveyed regions had a lower probability of selection than those in sparsely surveyed regions [48]. The Bayesian geostatistical models were then repeated without the validation dataset. Predicted Pf PR values from the Bayesian geostatistical models were compared to actual Pf PR values observed at the validation locations using the mean error (ME), mean absolute error (MAE) and the area under the curve (AUC) of the receiver operating characteristic (ROC). ME is a measure of the bias of predictions (the overall tendency to over or under predict) whilst MAE is a measure of overall precision (the average magnitude of error in individual predictions) and AUC is a measure of discriminatory ability of predictions with respect to a true prevalence threshold (observed endemicity classes). AUC values greater than 0.9 indicate excellent discrimination; >0.7 moderate discrimination; >0.5 poor discrimination; and <0.5, the model does not discriminate any more successfully than random allocation of test status [49, 50].
Ethical approval
Ethical approval was provided through permission by the Ministry of Health Somalia, Transitional Federal Government of Somalia Republic, Ref: MOH/WC/XA/146./07, dated 02/02/07. Informed verbal consent was sought from all participating households and individuals.
Results
Sample description
Summary of Somalia survey data by source for the North and South zones.
Survey source  

Zone  FAO/FSAU n (%)  WHO n (%)  Total n (%) 
North  
Number survey locations sampling with 40+ people  64  61  125 
Number georeferenced  55 (85.9%)  60 (98.4%)  115 (92.0%) 
Number identified as outliers  0 (0.0%)  2 (3.3%)  2 (1.6%) 
Number selected for model*  55 (85.9%)  58 (95.1%)  113 (90.4%) 
Number of surveys with zero Pf PR**  32 (50.0%)  26 (42.6%)  58 (46.4%) 
Population sample size  5,213  5,255  10,468 
Number Pf PR positive  97  196  293 
Mean (Median) Pf PR (%)  1.8 (0.0)  3.7 (1.1)  2.8 (0.0) 
IQR Pf PR (%)  (0.0, 3.1)  (0.0, 4.0)  (0.0, 4.0) 
South  
Number survey locations sampling 40+ people  311  64  375 
Number georeferenced  279 (89.7%)  58 (90.6%)  337 (89.9%) 
Number identified as outliers  1 (0.3%)  0 (0.0%)  1 (0.3%) 
Number selected for model*  278 (89.4%)  58 (90.6%)  336 (89.6%) 
Number of surveys with zero Pf PR**  73 (23.5%)  23 (35.9%)  96 (25.6%) 
Population sample size  16,048  3,963  20,011 
Number Pf PR positive  2,081  208  2,289 
Mean (Median) Pf PR (%)  13.0 (4.0)  5.2 (2.0)  11.4 (3.0) 
IQR Pf PR (%)  (0.0,10.0)  (0.0, 6.0)  (0.0, 9.0) 
Nonspatial bivariate and multivariate analysis of covariates
Nonspatial bivariate and multivariate analysis of the association of survey and environmental covariates with Pf PR in north and south of Somalia.
Bivariate model  Multivariate model  

Zone  Odds ratio (95% Confidence Interval)  Pvalue  Odds ratio (95% Confidence Interval)  Pvalue 
North  
Average annual Enhanced Vegetation Index  1.03 (1.06–1.11)  <0.001  1.97 (0.24–1.63)  0.223 
Average annual precipitation  0.98 (0.97–0.99)  0.002  1.04 (1.02–1.08)  0.002 
Average annual minimum temperature  
<median of 20.4°C  Ref    Ref   
>median of 20.4°C  2.02 (1.59–2.58)  <0.001  1.35 (1.01–1.80)  0.045 
Average annual maximum temperature  
<median of 32.4°C  Ref    Ref   
>median of 32.4°C  2.63 (2.04–3.39)  <0.001     
Distance to water features (km)  0.61 (0.49–.76)  <0.001  0.68 (0.46–0.99)  0.049 
Survey month  
February  Ref    Ref   
July  2.83 (2.24–3.59)  <0.001  3.24 (2.37–4.44)  <0.001 
September  0.08 (0.04–0.16)  <0.001  0.10 (0.04–0.19)  <0.001 
November  1.66 (1.29–2.14)  <0.001  1.58 (1.13–2.22)  0.008 
South  
Average annual Enhanced Vegetation Index  1.09 (1.05–1.19)  <0.001  1.60 (0.54–4.70)  0.215 
Average annual precipitation  1.03 (1.02–1.04)  <0.001  1.02 (1.03–1.04)  <0.001 
Average annual minimum temperature  
<median of 22.1°C  Ref    Ref  
>median of 22.1°C  0.43 (0.39–0.48)  <0.001  0.61 (0.55–0.68)  <0.001 
Average annual maximum temperature  
<median of 33.6°C  Ref       
>median of 33.6°C  2.15 (1.96–2.36)  <0.001     
Distance to water features (km)  1.04 (1.01–1.07)  0.005  0.84 (0.81–0.87)  <0.001 
Survey month  
February  Ref       
March  3.57 (3.25–3.92)  <0.001  7.62 (6.30–9.22)  <0.001 
May  1.11 (0.95–1.30)  0.194  6.03 (4.68,7.79)  <0.001 
June  0.73 (0.65–0.82)  <0.001  1.71 (1.43–2.04)  0.001 
November  0.18 (0.14–0.24)  <0.001  0.33 (0.25–0.45)  <0.001 
December  1.10 (0.99–1.22)  0.056  1.62 (1.41–1.87)  <0.001 
Summary output of Bayesian geostatistical models for the north and south of Somalia.
Model/Variables  North  South 

Bayesian geostatistical model (no covariates)  
α (Intercept)  4.62 (5.44, 4.33)  2.9 (3.37, 2.27) 
ϕ (Decay of spatial correlation (degrees latitude and longitude))  8.90 (3.11, 12.75)  4.79 (2.11, 6.97) 
σ^{2} (Variance of spatial process)  4.35 (271, 7.14)  7.14 (5.00, 8.76) 
DIC  326  1,454 
ME (% Pf PR)  3.83  4.14 
MAE (% Pf PR)  4.12  5.06 
AUROC*  
<5% Pf PR  0.72 (0.64, 0.86)  0.87 (0.72, 0.91) 
5–39% Pf PR  0.66 (0.51, 0.80)  0.78 (0.66, 0.85) 
≥ 40% Pf PR  NA  0.56 (0.37, 0.73) 
Bayesian geostatistical model (with covariates)  
α (Intercept)  4.62 (5.23, 4.10)  2.86 (3.79, 2.27) 
ϕ (Decay of spatial correlation)  10.35 (4.70, 12.88)  5.78 (2.95, 6.99) 
σ^{2} (Variance of spatial process)  3.70 (2.17, 7.14)  5.00 (3.70, 6.70) 
DIC  323  1,429 
ME (% Pf PR)  2.56  3.65 
MAE (% Pf PR)  4.75  5.00 
AUROC*  
<5% Pf PR  0.75 (0.64, 0.91)  0.91 (0.87, 0.99) 
5–39% Pf PR  0.64 (0.43, 0.84)  0.81 (0.70, 0.94) 
≥ 40% Pf PR  NA  0.51 (0.32, 0.83) 
Odds ratio, (95% Bayes credible interval)  
Month of survey  
Feb  Ref  Ref 
Mar    4.06 (2.20, 7.63) 
Jun    
Jul  3.25 (0.91, 11.36)  0.87 (0.48,1.46) 
Sep  0.2 (0.02, 1.74)  
Nov  1.31 (0.33, 4.36)  0.48 (0.23, 0.96) 
Dec    1.95 (0.91, 3.90) 
Annual average minimum temperature  
<median of 20.4/22.1 (North/South)°C  Ref   
>median of 20.4/22.1(North/South)°C  1.12 (0.84, 1.33)  0.83 (0.67,0.96) 
Annual average precipitation  1.70(0.53, 5.44)  1.41 (1.07, 1.94) 
Distance to water features (km)  1.22 (0.53, 2.81)  0.79 (0.74, 1.29) 
Bayesian geostatistical models
North
The Bayesian geostatistical model without covariates in the north had σ^{2} (variance of spatial process) with a posterior median of 4.35 (95% credible interval (CI): 2.71, 7.14); ϕ (rate of decay of spatial correlation) of 8.90 (95% CI: 3.11, 12.75); a DIC value (measure of model fit) of 326; a ME (measure of model bias) of 3.83% Pf PR; and a MAE (measure of model precision) of 4.12% Pf PR (Table 3). The results of the multivariate Bayesian geostatistical model showed that none of the selected covariates remained significant (odds 95% CI included 1) after accounting for spatial correlation (Table 3). The DIC of the multivariate model was 323 and only marginally lower than that of the model without covariates, implying that the inclusion of the covariates did not improve overall model fit in the north. However, the covariates did account for some of the spatial variation in the data with spatial variance (σ^{2}) decreasing from 4.35 to 3.70 (Table 3). Although the model with covariates exhibited lower bias (ME = 2.56% Pf PR) it also had marginally lower precision (MAE = 4.75% Pf PR) compared to the model without covariates. AUC values were similar for both models and indicated acceptable overall fit for endemicity class <5% Pf PR (AUC values > 0.70) but less so for endemicty class 5–39% Pf PR (AUC values of < 0.70). There were insufficient data points in the validation set to compute AUC values for the endemicity class ≥ 40% Pf PR (Table 3).
South
In the south the posterior median variance of the spatial process for the model without covariates was 7.14 (95% Bayes CI: 5.00, 8.76) and that of the rate of spatial decay parameter was 4.79 (95% Bayes CI: 2.11, 6.79). For the model with covariates the month of survey, annual average maximum temperature and precipitation all remained significant explanatory factors for Pf PR (Table 3). Odds of P. falciparum infection were higher in March (OR: 4.06, 95% CI 2.20–7.63) relative to February; and with increasing precipitation (OR: 1.41, 1.07–1.94). Higher minimum temperatures (OR: 0.83, 0.67–0.96) and a survey month of November relative to February (OR 0.48, 0.23–0.96) both reduced the odds of P. falciparum infection (Table 3). The inclusion of the covariates improved model fit with DIC of 1,429 compared to 1,454 for the model without covariates. There were no clear differences, however, between the models with and without covariates in the other parameters of model assessment with values of ME (3.65% vs 4.14% Pf PR); MAE (5.00% vs 5.06% Pf PR) and AUC values (<5% Pf PR: 0.91 vs 0.87; 5–39% Pf PR: 0.81 vs 0.78; ≥ 40% Pf PR: 0.51 vs 0.56). Similar to the multivariate model in the south, most of the spatial residual variation remained unexplained by the covariates.
Overall, the models for the south (with or without covariates) had higher spatial variances and spatial autocorrelation occurred over larger distances compared to the north (Table 3). In addition, models in the south exhibited better model fit with AUC values higher across all endemicity classes probably due to greater availability and better distribution of data in this zone (Figure 2).
Predicted (posterior median) Pf PR maps
Discussion
There has been little historical description of the basic epidemiology of malaria transmission in Somalia. In 2002, an application was made to and successfully approved by the GFATM to support the funding of a suite of interventions and strategies managed by a consortium of nongovernment and governmental agencies across the three main zones of Somalia [51]. This application, similar to other successful applications and RBM policies in neighbouring, higherintensity transmission countries of Kenya [52], Tanzania [53] and Uganda [54] involved a monitoring & evaluation component to investigate intervention coverage and P. falciparum infection rates. In Somalia rapid, sample malaria intervention and parasitological surveys of communities have now become part of a routine component of rolling nutritional surveillance surveys across the country [55]. Consequently, despite being a country without a functioning research capacity and a fragile health system, Somalia is now one of the 87 P. falciparum endemic countries worldwide with the largest series of infection prevalence data [56, 57].
Simple summaries of the data suggest that large parts of the country, particularly in the north, have very low human infection prevalence (Table 1). These summaries, however, mask spatial heterogeneities in risk that are important for better targeting of interventions and maintaining aggressive surveillance. A Bayesian geostatistical approach to predict Pf PR throughout Somalia is used here. In the north, the inclusion of the survey and environmental covariates appeared not to make a significant difference to model fit, while in the south they improved the model fit. Predictions of endemicity class membership made in the north were associated with lower prediction probabilities and generated generally lower AUROC values (Table 3 & Figure 3c). This greater prediction uncertainty in the north is due largely to the comparatively fewer empirical data points compared to the south (Figure 2). This disparity was essentially driven by the population distribution: approximately 65% of Somalia's population live in the South and communities in the North are more scattered in isolated settlements [58].
Although the environmental covariates selected for inclusion in the Bayesian geostatistical model were significantly associated with Pf PR when examined in the nonspatial multivariate model (Tables 2 &3), none remained significant when spatial correlation was accounted for in the north and only precipitation and temperature remained significant in south. Overall the inclusion of these covariates accounted for a relatively small proportion of spatial variation suggesting that other unmeasured factors might be influencing the spatial distribution of malaria prevalence. These factors might include proximity to artificial breeding sites such as wells, dams, boreholes and seasonal streams and/or the use of interventions to prevent malaria at the household level. It has recently been demonstrated that in southern Somalia, the use of insecticide treated nets (ITN) reduced the prevalence of infection by as much as 54% [30]. Mapping the household or community levels of ITN use at high spatial resolutions is not currently feasible at a national scale. Similarly, the mapping of fluctuating, localized vector breeding sites would require very detailed spatial reconnaissance and infection mapping at finer scales than is currently possible using public domain covariate data at national scales. Furthermore, communities where sample sizes were less than 40, most of which could not be geolocated, were excluded from the analysis and these might have resulted in information loss for some areas of Somalia. Although the difference, in terms of mean parasite prevalence, was minimal between the excluded and included surveys, future analysis should include all data regardless of sample sizes given the Bayesian analytical approach implicitly adjusts for sample size.
Despite the constraints described above, the use of Bayesian geostatistics to model Pf PR does provide a valuable method to define subnational spatial variation in prevalence, and a baseline against which future changes in prevalence can be quantified intervention coverage expands. Under such a scenario the value of the environmental covariates might be expected to wane further, particularly in areas of very low transmission intensity where the environment currently supports homogenously low transmission conditions. The similar levels of performance observed between the univariate and multivariate models for the north of Somalia may be evidence of this view. In addition, the relatively higher coverage of ITN among the communities closest to the two rivers in the south might explain the lower predicted prevalence in their immediate vicinity consistent with the observational data and reported effectiveness of ITN [30].
Population density or a derived categorisation of urbanisation, with known influences on malaria transmission [59, 60], would have been a worthy candidate covariate for testing in this study and in determining accurately the population at risk against varying malaria endemicity. However, the reliability of settlement and population data in Somalia is highly questionable. The last national census was undertaken in 1971 and the displacement and migration over the last 20 years of civil unrest has been substantial. Development agencies and nongovernmental agencies working in Somalia continue to update a semiquantitative database of settlement locations and population counts but its fidelity is unknown. The absence of an accurate national census also hampers the linkage of spatial malaria risk to populationsexposed to risk. Notwithstanding the precision and scale of calculating populations at risk, aggregated districtlevel estimates of population in 2004 across the 120 districts of Somalia have been used and assigned each district the dominant Pf PR risk class. From these numbers it can be estimated that approximately 75% of Somalia's estimated 7.4 million people live in areas that support unstable or very low Pf PR (0–5%) transmission and less than 0.1% live in areas classified as high, intense transmission (Pf PR > 40%). Areas of low Pf PR include many communities where infection prevalence was observed as zero (Table 1). In these locations it is assumed that these observations represent a statistical zero (i.e. resulting from a limited sample in areas of very low transmission) rather than implying a true absence of infection risk [56]. This is important to highlight because routine sample surveys in such areas demand considerably larger samples [45, 61] or the use of serological markers of parasite exposure [62] to truly exclude the possibility of transmission.
In communities exposed to low Pf PR, such as the majority of the population in Somalia, the risk of disease is low and spread across all agegroups. These are fundamentally different epidemiological conditions to areas of high transmission where functional immunity is developed early in life and a higher disease burden is experienced in young children and pregnant women [63–66]. Tailoring the existing intervention recommendations in the Somalia National Malaria Strategy [67] to the spatial transmission patterns shown in Figure 3 will be a challenge to the agencies providing malaria control services nationwide.
Conclusion
The use of routine, nationwide surveillance of infection prevalence is key to monitoring the changing epidemiology of malaria in all countries scaling up coverage of malaria preventative strategies. Including RDTs in ongoing communitybased health surveillance is a costeffective means of assembling this information. The use of geostatistical methods can help focus surveillance efforts and define those areas where uncertainty exists, guiding future sampling [49, 68]. Coupled with better estimates of where people live, these should provide the basis for informed estimates of disease burden [63] and how these might change with changing infectionrisk exposure. Somalia has a range of political and economic barriers that might limit the success of a strategic, epidemiologically driven malaria control programme. It has been possible to demonstrate, however, that the foci of greatest disease risk are predominantly concentrated in one area in the South and that infection risks are very low in the northern reaches of the country. Moreover, although the density of survey sites and hence the uncertainty of the modelled output varies spatially, also it has been demonstrated that, despite constant civil disturbance, routine survey data can be assembled to inform strategic decision making. Finally, areas where model uncertainties are greatest, predominantly in the north of the country, should be the focus of any future parasitological surveys to improve further the precision of the prevalence maps.
Abbreviations
 AIC:

Akaike Information Criterion
 AUCROC:

Area under the curve of the receiver operating characteristic
 DIC:

Deviance Information Criterion
 EVI:

Enhanced Vegetation Index
 FAO/FSAU:

Food Agricultural OrganizationFood Security Analysis Unit (FSAU)
 GFATM:

Global Fund to Fight Aids TB and Malaria
 ITN:

Insecticide treated nets
 MAE:

Mean absolute error
 MAP:

The Malaria Atlas Project
 MCMC:

Markov Chain Monte Carlo
 ME:

Mean Error
 MODIS:

Moderate Resolution Imaging Spectroradiometer
 Pf PR:

Plasmodium falciparum parasite rate
 RDT:

Rapid Diagnostics Test
 SSA:

subSaharan Africa
 UNICEF:

United Nations Children Fund
 WHO:

World Health Organization.
Declarations
Acknowledgements
The authors are grateful to the WHOMERLIN and FAO/FSAU survey team for their invaluable supervision and support during the field surveys and subsequent data entry and cleaning and Bruno Moonen of MERLIN specifically for helping with training for the parasite survey. We thank Priscilla Gikandi and Victor Alegana for additional data cleaning and georeferencing. We are also grateful to Anand Patil and Andy Tatem for statistical and GIS advise and to Simon Brooker, Carlos Guerra and Emelda Okiro for their comments on the manuscript.
Funding for the WHOMERLIN 2005 surveys were provided by the UN Trust Fund for Human Security and the GFATM. FAO/FSAU funded training of assessment teams, data collection, paid enumerators and data entry clerks for the 2007 surveys. The FAO/FSAU nutrition surveillance project is funded primarily by OFDAUSAID and receives support from UNICEF, SIDA and EC for conducting nutrition assessments in Somalia. RDTs and antimalarial treatment were provided by UNICEF through GFATM funding (SOM202G01M00). AMN is supported by the Wellcome Trust as a Research Training Fellow (#081829). SIH is supported by the Wellcome Trust as Senior Research Fellow (#079091). RWS is supported by the Wellcome Trust as Principal Research Fellow (#079081). AMN, SIH and RWS acknowledge the support of the Kenyan Medical Research Institute. The funders did not have a role in study design, data collection and analysis, decision to publish, or preparation of manuscript. This work forms part of the output of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk), principally funded by the Wellcome Trust, U.K.
Authors’ Affiliations
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