A Long Neglected World Malaria Map: Plasmodium vivax
Endemicity in 2010
Peter W. Gething1*, Iqbal R. F. Elyazar2, Catherine L. Moyes1, David L. Smith3,4, Katherine E. Battle1,
Carlos A. Guerra1, Anand P. Patil1, Andrew J. Tatem4,5, Rosalind E. Howes1, Monica F. Myers1,
Dylan B. George4, Peter Horby6,7, Heiman F. L. Wertheim6,7, Ric N. Price7,8,9, Ivo Mu ¨eller10, J.
Kevin Baird2,7, Simon I. Hay1,4*
1Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom, 2Eijkman-Oxford Clinical Research Unit, Jakarta,
Indonesia, 3Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America, 4Fogarty
International Center, National Institutes of Health, Bethesda, Maryland, United States of America, 5Department of Geography and Emerging Pathogens Institute,
University of Florida, Gainesville, Florida, United States of America, 6Oxford University Clinical Research Unit - Wellcome Trust Major Overseas Programme, Ho Chi Minh
City, Vietnam, 7Nuffield Department of Medicine, Centre for Tropical Medicine, University of Oxford, Oxford, United Kingdom, 8Global Health Division, Menzies School of
Health Research, Charles Darwin University, Darwin, Northern Territory, Australia, 9Division of Medicine, Royal Darwin Hospital, Darwin, Northern Territory, Australia,
10Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea
Background: Current understanding of the spatial epidemiology and geographical distribution of Plasmodium vivax is far
less developed than that for P. falciparum, representing a barrier to rational strategies for control and elimination. Here we
present the first systematic effort to map the global endemicity of this hitherto neglected parasite.
Methodology and Findings: We first updated to the year 2010 our earlier estimate of the geographical limits of P. vivax
transmission. Within areas of stable transmission, an assembly of 9,970 geopositioned P. vivax parasite rate (PvPR) surveys
collected from 1985 to 2010 were used with a spatiotemporal Bayesian model-based geostatistical approach to estimate
endemicity age-standardised to the 1–99 year age range (PvPR1–99) within every 565 km resolution grid square. The model
incorporated data on Duffy negative phenotype frequency to suppress endemicity predictions, particularly in Africa.
Endemicity was predicted within a relatively narrow range throughout the endemic world, with the point estimate rarely
exceeding 7% PvPR1–99. The Americas contributed 22% of the global area at risk of P. vivax transmission, but high endemic
areas were generally sparsely populated and the region contributed only 6% of the 2.5 billion people at risk (PAR) globally.
In Africa, Duffy negativity meant stable transmission was constrained to Madagascar and parts of the Horn, contributing
3.5% of global PAR. Central Asia was home to 82% of global PAR with important high endemic areas coinciding with dense
populations particularly in India and Myanmar. South East Asia contained areas of the highest endemicity in Indonesia and
Papua New Guinea and contributed 9% of global PAR.
Conclusions and Significance: This detailed depiction of spatially varying endemicity is intended to contribute to a much-
needed paradigm shift towards geographically stratified and evidence-based planning for P. vivax control and elimination.
Citation: Gething PW, Elyazar IRF, Moyes CL, Smith DL, Battle KE, et al. (2012) A Long Neglected World Malaria Map: Plasmodium vivax Endemicity in 2010. PLoS
Negl Trop Dis 6(9): e1814. doi:10.1371/journal.pntd.0001814
Editor: Jane M. Carlton, New York University, United States of America
Received April 24, 2012; Accepted July 29, 2012; Published September 6, 2012
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: SIH is funded by a Senior Research Fellowship from the Wellcome Trust (#095066), which also supports PWG, CAG, and KEB. CLM and APP are funded
by a Biomedical Resources Grant from the Wellcome Trust (#091835). REH is funded by a Biomedical Resources Grant from the Wellcome Trust (#085406). IRFE is
funded by grants from the University of Oxford—Li Ka Shing Foundation Global Health Program and the Oxford Tropical Network. DLS and AJT are supported by
grants from the Bill and Melinda Gates Foundation (#49446, #1032350) (http://www.gatesfoundation.org). PH is supported by Wellcome Trust grants 089276/Z/
09/Z and the Li Ka Shing Foundation. RNP is a Wellcome Trust Senior Fellow in Clinical Science (#091625). JKB is supported by a Wellcome Trust grant
(#B9RJIXO). PWG, APP, DLS, AJT, DBG, and SIH also acknowledge support from the RAPIDD program of the Science and Technology Directorate, Department of
Homeland Security, and the Fogarty International Center, National Institutes of Health (http://www.fic.nih.gov). 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, UK (http://www.wellcome.ac.uk). MAP also acknowledges the support of
the Global Fund to Fight AIDS, Tuberculosis, and Malaria (http://www.theglobalfund.org). The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (PWG); firstname.lastname@example.org (SIH)
The international agenda shaping malaria control financing,
research, and implementation is increasingly defined around the
goal of regional elimination [1–6]. This ambition ostensibly
extends to all human malarias, but whilst recent years have seen a
surge in research attention for Plasmodium falciparum, the knowl-
edge-base for the other major human malaria, Plasmodium vivax, is
far less developed in almost every aspect [7–11]. During 2006–
2009 just 3.1% of expenditures on malaria research and
development were committed to P. vivax . The notion that
control approaches developed primarily for P. falciparum in
PLOS Neglected Tropical Diseases | www.plosntds.org1September 2012 | Volume 6 | Issue 9 | e1814
holoendemic Africa can be transferred successfully to P. vivax is,
however, increasingly acknowledged as inadequate [13–17].
Previous eradication campaigns have demonstrated that P. vivax
frequently remains entrenched long after P. falciparum has been
eliminated . The prominence of P. vivax on the global health
agenda has risen further as evidence accumulates of its capacity in
some settings to cause severe disease and death [19–25], and of the
very large numbers of people living at risk .
Amongst the many information gaps preventing rational
strategies for P. vivax control and elimination, the absence of
robust geographical assessments of risk has been identified as
particularly conspicuous [9,27]. The endemic level of the disease
determines its burden on children, adults, and pregnant women;
the likely impact of different control measures; and the relative
difficulty of elimination goals. Despite the conspicuous impor-
tance of these issues, there has been no systematic global
assessment of endemicity. The Malaria Atlas Project was initiated
in 2005 with an initial focus on P. falciparum that has led to global
maps [28–30] for this parasite being integrated into policy
planning at regional to international levels [4,31–36]. Here we
present the outcome of an equivalent project to generate a
comprehensive evidence-base on P. vivax infections worldwide,
and to generate global risk maps for this hitherto neglected
disease. We build on earlier work  defining the global range
of the disease and broad classifications of populations at risk to
now assess the levels of endemicity under which these several
billion people live. This detailed depiction of geographically
varying risk is intended to contribute to a much-needed paradigm
shift towards geographically stratified and evidence-based plan-
ning for P. vivax control and elimination.
Numerous biological and epidemiological characteristics of P.
vivax present unique challenges to defining and mapping metrics of
risk. Unlike P. falciparum, infections include a dormant hypnozoite
liver stage that can cause clinical relapse episodes [37,38]. These
periodic events manifest as a blood-stage infection clinically
indistinguishable from a primary infection and constitute a
substantial, but geographically varying, proportion of total patent
infection prevalence and disease burden within different popula-
tions [37,39–41]. The parasitemia of P. vivax typically occurs at
much lower densities compared to those of falciparum malaria,
and successful detection by any given means of survey is much less
likely. Another major driver of the global P. vivax landscape is the
influence of the Duffy negativity phenotype . This inherited
blood condition confers a high degree of protection against P. vivax
infection and is present at very high frequencies in the majority of
African populations, although is rare elsewhere . These
factors, amongst others, mean that the methodological framework
for mapping P. vivax endemicity, and the interpretation of the
resulting maps, are distinct from those already established for P.
falciparum [28,29]. The effort described here strives to accommo-
date these important distinctions in developing a global distribu-
tion of endemic vivax malaria.
The modelling framework is displayed schematically in
Figure 1. In brief, this involved (i) updating of the geographical
limits of stable P. vivax transmission based on routine reporting
data and biological masks; (ii) assembly of all available P. vivax
parasite rate data globally; (iii) development of a Bayesian model-
based geostatistical model to map P. vivax endemicity within the
limits of stable transmission; and (iv) a model validation
procedure. Details on each of these stages are provided below
with more extensive descriptions included as Protocols S1, S2, S3,
Updating Estimates of the Geographical Limits of
Endemic Plasmodium vivax in 2010
The first effort to systematically estimate the global extent of P.
vivax transmission and define populations at risk was completed in
2009 . As a first step in the current study, we have updated this
work with a new round of data collection for the year 2010. The
updated data assemblies and methods are described in full in
Protocol S1. In brief, this work first involved the identification of
95 countries as endemic for P. vivax in 2010. From these, P. vivax
annual parasite incidence (PvAPI) routine case reports were
assembled from 17,893 administrative units . These PvAPI
and other medical intelligence data were combined with remote
sensing surfaces and biological models  that identified areas
where extreme aridity or temperature regimes would limit or
preclude transmission (see Protocol S1). These components were
combined to classify the world into areas likely to experience zero,
unstable (PvAPI ,0.1% per annum), or stable (PvAPI $0.1% per
annum) P. vivax transmission. Despite the very high population
frequencies of Duffy negativity across much of Africa, the presence
of autochthonous transmission of P. vivax has been confirmed by a
systematic literature review for 42 African countries . We
therefore treated Africa in the same way as elsewhere in this initial
stage: regions were deemed to have stable P. vivax transmission
unless the biological mask layers or PvAPI data suggested
Plasmodium vivax is one of five parasites causing malaria in
humans. Whilst it is found across a larger swathe of the
globe and potentially affects a larger number of people
than its more notorious cousin, Plasmodium falciparum, it
receives a tiny fraction of the research attention and
financing: around 3%. This neglect, coupled with the
inherently more complex nature of vivax biology, means
important knowledge gaps remain that limit our current
ability to control the disease effectively. This patchy
knowledge is becoming recognised as a cause for concern,
in particular as the global community embraces the
challenge of malaria elimination which, by definition,
includes P. vivax and the other less common Plasmodium
species as well as P. falciparum. Particularly conspicuous is
the absence of an evidence-based map describing the
intensity of P. vivax endemicity in different parts of the
world. Such maps have proved important for other
infectious diseases in supporting international policy
formulation and regional disease control planning, imple-
mentation, and monitoring. In this study we present the
first systematic effort to map the global endemicity of P.
vivax. We assembled nearly 10,000 surveys worldwide in
which communities had been tested for the prevalence of
P. vivax infections. Using a spatial statistical model and
additional data on environmental characteristics and Duffy
negativity, a blood disorder that protects against P. vivax,
we estimated the level of infection prevalence in every
565 km grid square across areas at risk. The resulting
maps provide new insight into the geographical patterns
of the disease, highlighting areas of the highest endemic-
ity in South East Asia and small pockets of Amazonia, with
very low endemic setting predominating in Africa. This
new level of detailed mapping can contribute to a wider
shift in our understanding of the spatial epidemiology of
this important parasite.
Global Plasmodium vivax Endemicity in 2010
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Creating a Database of Georeferenced PvPR Data
As with P. falciparum, the most globally ubiquitous and
consistently measured metric of P. vivax endemicity is the parasite
rate (PvPR), defined as the proportion of randomly sampled
individuals in a surveyed population with patent parasitemia in
their peripheral blood as detected via, generally, microscopy or
rapid diagnostic test (RDT). Whilst RDTs can provide lower
sensitivity and specificity than conventional blood smear micros-
copy, and neither technique provides accuracy comparable to
molecular diagnostics (such as polymerase chain reaction, PCR),
the inclusion of both microscopically and RDT confirmed parasite
rate data was considered important to maximise data availability
and coverage across the endemic world.
To map endemicity within the boundaries of stable transmis-
sion, we first carried out an exhaustive search and assembly of
georeferenced PvPR survey data from formal and informal
literature sources and direct communications with data generating
organisations . Full details of the data search strategy,
abstraction and inclusion criteria, geopositioning and fidelity
checking procedure are included in Protocol S2. The final
database, completed on 25thNovember 2011, consisted of 9,970
quality-checked and spatiotemporally unique data points, span-
ning the period 1985–2010. Figure 2A maps the spatial
distribution of these data and further summaries by survey origin,
georeferencing source, time period, age group, sample size, and
type of diagnostic used are provided in Protocol S2.
Modelling Plasmodium vivax Endemicity within Regions
of Stable Transmission
We adopt model-based geostatistics (MBG) [47,48] as a robust
and flexible modelling framework for generating continuous
surfaces of malaria endemicity based on retrospectively assembled
parasite rate survey data [28,29,49]. MBG models are a special
class of generalised linear mixed models, with endemicity values at
each target pixel predicted as a function of a geographically-
varying mean and a weighted average of proximal data points.
The mean can be defined as a multivariate function of
environmental correlates of disease risk. A covariance function is
used to characterise the spatial or space-time heterogeneity in the
observed data, which in turn is used to define appropriate weights
assigned to each data point when predicting at each pixel. This
framework allows the uncertainty in predicted endemicity values
to vary between pixels, depending on the observed variation,
density and sample size of surveys in different locations and the
predictive utility of the covariate suite. Parts of the map where
survey data are dense, recent, and relatively homogenous will be
predicted with least uncertainty, whilst regions with sparse or
mainly old surveys, or where measured parasite rates are
extremely variable, will have greater uncertainty. When MBG
models are fitted using Bayesian inference and a Markov chain
Monte Carlo (MCMC) algorithm, uncertainty in the final
predictions as well as all model parameters can be represented
in the form of predictive posterior distributions .
Figure 1. Schematic overview of the mapping procedures and methods for Plasmodium vivax endemicity. Blue boxes describe input
data. Orange boxes denote models and experimental procedures; green boxes indicate output data (dashed lines represent intermediate outputs
and solid lines final outputs). U/R=urban/rural; UNPP=United Nations Population Prospects. Labels S1-4 denote supplementrary information in
Protocols S1, S2, S3, and S4.
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We developed for this study a modified version of the MBG
framework used previously to model P. falciparum endemicity
[28,29], with some core aspects of the model structure remaining
unchanged and others altered to capture unique aspects of P. vivax
biology and epidemiology. The model is presented in full in
Protocol S3. As in earlier work [28,29,49], we adopt a space-time
approach to allow surveys from a wide time period to inform
predictions of contemporary risk. This includes the use of a
spatiotemporal covariance function which is parameterised to
downweight older data appropriately. We also retain a seasonal
component in the covariance function, although we note that
seasonality in transmission is often only weakly represented in
PvPR in part because of the confounding effect of relapses
occurring outside peak transmission seasons . A minimal set of
covariates were included to inform prediction of the mean
function, based on a priori expectations of the major environmental
factors modulating endemicity. These were (i) an indicator variable
defining areas as urban or rural based on the Global Rural Urban
Mapping Project (GRUMP) urban extent product [52,53]; (ii) a
long-term average vegetation index product as an indicator of
overall moisture availability for vector oviposition and survival
[54,55]; and (iii) a P. vivax specific index of temperature suitability
derived from the same model used to delineate suitable areas on
the basis of vector survival and sporogony .
Our assembly of PvPR surveys was collected across a variety of
age ranges and, since P. vivax infection status can vary
systematically in different age groups within a defined community,
it was necessary to standardise for this source of variability to allow
all surveys to be used in the same model. We adopted the same
model form as has been described  and used previously for P.
falciparum [28,29], whereby population infection prevalence is
expected to rise rapidly in early infancy and plateau during
childhood before declining in early adolescence and adulthood.
The timing and relative magnitude of these age profile features are
Figure 2. The spatial distribution of Plasmodium vivax malaria endemicity in 2010. Panel A shows the 2010 spatial limits of P. vivax malaria
risk defined by PvAPI with further medical intelligence, temperature and aridity masks. Areas were defined as stable (dark grey areas, where PvAPI
$0.1 per 1,000 pa), unstable (medium grey areas, where PvAPI ,0.1 per 1,000 pa) or no risk (light grey, where PvAPI=0 per 1,000 pa). The
community surveys of P. vivax prevalence conducted between January 1985 and June 2010 are plotted. The survey data are presented as a
continuum of light green to red (see map legend), with zero-valued surveys shown in white. Panel B shows the MBG point estimates of the annual
mean PvPR1–99for 2010 within the spatial limits of stable P. vivax malaria transmission, displayed on the same colour scale. Areas within the stable
limits in (A) that were predicted with high certainty (.0.9) to have a PvPR1–99less than 1% were classed as unstable. Areas in which Duffy negativity
gene frequency is predicted to exceed 90%  are shown in hatching for additional context.
Global Plasmodium vivax Endemicity in 2010
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likely distinct between the two parasites in different endemic
settings [51,57], and so the model was parameterised using an
assembly of 67 finely age-stratified PvPR surveys (Protocol S2),
with estimation carried out in a Bayesian model using MCMC.
The parameterised model was then used to convert all observed
survey prevalences to a standardised age-independent value for use
in modelling, and then further allowed the output prevalence
predictions to be generated for any arbitrary age range. We chose
to generate maps of all-age infection prevalence, defined as
individuals of age one to 99 years (thus PvPR1–99). We excluded
infection in those less than one year of age from the standardisa-
tion because of the confounding effect of maternal antibodies, and
because parasite rate surveys very rarely sample young infants. We
deviated from the two-to-ten age range used for mapping P.
falciparum [28,29] because the relatively lower prevalences has
meant that surveys are far more commonly carried out across all
Incorporating Duffy Negativity
Since Duffy negative individuals are largely refractory to P. vivax
infection , high population frequencies of this phenotype have
a dramatic suppressing effect on endemicity, even where
conditions are otherwise well suited for transmission . The
predominance of Duffy negativity in Africa has led to a historical
perception that P. vivax is absent from much of the continent, and a
dearth of surveys or routine diagnoses testing for the parasite have
served to entrench this mantra . However, evidence exists of
autochthonous P. vivax transmission across the continent , and
therefore we did not preclude any areas at risk a priori. Instead, we
used a recent map of estimated Duffy negativity phenotypic
frequency  and incorporated the potential influence of this
blood group directly in the MBG modelling framework. The
mapped Duffy-negative population fraction at each location was
excluded from the denominator in PvPR survey data, such that
any P. vivax positive individuals were considered to have arisen
from the Duffy positive population subset. Thus in a location with
90% Duffy negativity, five positive individuals in a survey of 100
would give an assumed prevalence of 50% amongst Duffy
positives. Correspondingly, prediction of PvPR was then restricted
to the Duffy positive proportion at each pixel, with the final
prevalence estimate re-converted to relate to the total population.
This approach has two key advantages. First, predicted PvPR at
each location could never exceed the Duffy positive proportion,
therefore ensuring biological consistency between the P. vivax and
Duffy negativity maps. Second, where PvPR survey data were
sparse across much of Africa, the predictions could effectively
borrow strength from the Duffy negativity map because predic-
tions of PvPR were restricted to a much narrower range of possible
Model Implementation and Map Generation
The P. vivax endemic world was divided into four contiguous
regions with broadly distinct biogeographical, entomological and
epidemiological characteristics: the Americas and Africa formed
separate regions, whilst Asia was subdivided into Central and
South East sub-regions with a boundary at the Thailand-Malaysia
border (see Protocol S2). This regionalisation was implemented in
part to retain computational feasibility given the large number of
data points, but also to allow model parameterisations to vary and
better capture regional endemicity characteristics. Within each
region, a separate MBG model was fitted using a bespoke MCMC
algorithm  to generate predictions of PvPR1–99 for every
565 km pixel within the limits of stable transmission. The
prediction year was set to 2010 and model outputs represent an
annualised average across the 12 months of that year. Model
output consisted of a predicted posterior distribution of PvPR1–99
for every pixel. A continuous endemicity map was generated using
the mean of each posterior distribution as a point estimate. The
uncertainty associated with predictions was summarised by maps
showing the ratio of the posterior distribution inter-quartile range
(IQR) to its mean. The IQR is a simple measure of the precision
with which each PvPR value was predicted, and standardisation by
the mean produced an uncertainty index less affected by
underlying prevalence levels and more illustrative of relative
model performance driven by data densities in different locations.
This index was then also weighted by the underlying population
density to produce a second map indicative of those areas where
uncertainty is likely to be most operationally important.
Refining Limits Definition and Population at Risk
In some regions within the estimated limits of stable transmis-
sion, PvPR1–99was predicted to be extremely low, either because
of a dense abundance of survey data reporting zero infections or,
in Africa, because of very high coincident Duffy negativity
phenotype frequencies. Such areas are not appropriately described
as being at risk of stable transmission and so we defined a decision
rule whereby pixels predicted with high certainty (probability
.0.9) of being less than 1% PvPR1–99 were assigned to the
unstable class, thereby modifying the original transmission limits.
These augmented mapped limits were combined with a 2010
population surface derived from the GRUMP beta version [52,53]
to estimate the number of people living at unstable or stable risk
within each country and region. The fraction of the population
estimated to be Duffy negative  within each pixel was
considered at no risk and therefore excluded from these totals.
A model validation procedure was implemented whereby 10%
of the survey points in each model region were selected using a
spatially declustered random sampling procedure. These subsets
were held out and the model re-fitted in full using the remaining
90%. Model predictions were then compared to the hold-out data
points and a number of different aspects of model performance
were assessed using validation statistics described previously
[28,29]. The validation procedure is detailed in full in Protocol S4.
Full validation results are presented in Protocol S4. In brief,
examination of the mean error in the generation of the P. vivax
malaria endemicity point-estimate surface revealed minimal
overall bias in predicted PvPR with a global mean error of
20.41 (Americas 21.38, Africa 0.03, Central Asia 20.43, South
East Asia 20.43), with values in units of PvPR on a percentage
scale (see Protocol S4). The global value thus represents an overall
tendency to underestimate prevalence by just under half of one
percent. The mean absolute error, which measures the average
magnitude of prediction errors, was 2.48 (Americas 5.05, Africa
0.53, Central Asia 1.52, South East Asia 3.37), again in units of
PvPR (see Protocol S4).
Global Plasmodium vivax Endemicity and Populations at
Risk in 2010
The limits of stable and unstable P. vivax transmission, as defined
using PvAPI, biological exclusion masks and medical intelligence
Global Plasmodium vivax Endemicity in 2010
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data are shown in Figure 2A. The continuous surface of P. vivax
endemicity predicted within those limits is shown in Figure 2B.
The uncertainty map (posterior IQR:mean ratio) is shown in
Figure 3A and the population-weighted version in Figure 3B.
We estimate that P. vivax was endemic across some 44 million
square kilometres, approximately a third of the Earth’s land
surface. Around half of this area was located in Africa (51%) and a
quarter each in the Americas (22%) and Asia (27%) (Table 1).
However, the uneven distribution of global populations, coupled
with the protective influence of Duffy negativity in Africa, meant
that the distribution of populations at risk was very different. An
estimated 2.48 billion people lived at any risk of P. vivax in 2010
(Table 1), of which a large majority lived in Central Asia (82%)
with much smaller fractions in South East Asia (9%), the Americas
(6%), and Africa (3%). Of these, 1.52 billion lived in areas of
unstable transmission where risk is very low and case incidence is
unlikely to exceed one per 10,000 per annum. The remaining 964
million people at risk lived in areas of stable transmission,
representing a wide diversity of endemic levels. The global
distribution of populations in each risk class was similar to the total
at risk, such that over 80% of people in both classes lived in
Central Asia (Table 1).
Plasmodium vivax Endemicity in the Americas
Areas endemic for P. vivax in the Americas extended to some 9.5
million square kilometres, of which the largest proportion was in
the Amazonian region of Brazil (Figure 2B). Interestingly, only a
relatively small fraction of these areas (15%) experienced unstable
rather than stable transmission, suggesting a polarisation between
areas at stable risk and those where the disease is absent altogether
(Table 1). The regions of highest endemicity were found in
Amazonia and in Central America – primarily Nicaragua and
Honduras – with predicted mean PvPR1–99exceeding 7% in all
three locations. An important feature of P. vivax throughout the
Americas is that its distribution is approximately inverse to that of
the population. This is particularly true of the two most populous
endemic countries of the region, Brazil and Mexico, and it means
that, whilst the Americas contributed 53% of the land area
experiencing stable transmission worldwide, they housed only 5%
of the global population at that level of risk.
Figure 3. Uncertainty associated with predictions of Plasmodium vivax endemicity. Panel A shows the ratio of the posterior inter-quartile
range to the posterior mean prediction at each pixel. Large values indicate greater uncertainty: the model predicts a relatively wide range of PvPR1–99
as being equally plausible given the surrounding data. Conversely, smaller values indicate a tighter range of values have been predicted and, thus, a
higher degree of certainty in the prediction. Panel B shows the same index multiplied by the underlying population density and rescaled to 0–1 to
correspond to Panel A. Higher values indicate areas with high uncertainty and large populations.
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Uncertainty in predicted PvPR1–99was relatively high through-
out much of the Americas (Figure 3B). This reflects the
heterogeneous landscape of endemicity coupled with the generally
scarce availability of parasite rate surveys in the region (see
Figure 2A). However, when this uncertainty is weighted by the
underlying population density (Figure 3B), its significance on a
global scale is placed in context: because most areas at stable risk
are sparsely populated, the population-weighted uncertainty was
very low compared to parts of Africa and much of Asia.
Plasmodium vivax Endemicity in Africa, Yemen and Saudi
Our decision to assume stable transmission of P. vivax in Africa
unless robust PvAPI or biological mask data confirmed otherwise
meant that much of the continent south of the Sahara was initially
classified as being at stable risk (Figure 2A). However, by
implementing the MBG predictions of PvPR1–99throughout this
range and reclassifying a posteriori those areas likely to fall below an
endemicity threshold of 1% PvPR1–99, the majority of stable risk
areas were downgraded to unstable (Figure 2B). Thus, in the final
maps, 92% of endemic Africa was at unstable risk, with the
majority of Madagascar and Ethiopia, and parts of South Sudan
and Somalia making up most of the remaining area at stable risk.
Even in these areas, endemicity was uniformly low, with predicted
endemicity values rarely exceeding a point estimate of 2% PvPR1–
99. We augmented the final map with an additional overlay mask
delineating areas where Duffy negativity phenotype prevalence has
been predicted to exceed 90% (Figure 2B). The influence of this
blood group on the estimated populations at risk is profound: of
the 840 million Africans living in areas within which transmission
is predicted to occur, only 86 million were considered at risk,
contributing just 3% to the global total (Table 1).
Uncertainty in predicted PvPR1–99followed a similar pattern to
the magnitude of the predictions themselves (Figure 3B). Certainty
around the very low predicted endemicity values covering most of
the continent was extremely high – reflecting the increased precision
gained by incorporating the Duffy negativity information that
compensated for the paucity of P. vivax parasite rate surveys on the
continent. The pockets of higher endemicity in Madagascar and
northern East Africa were predicted with far less certainty. In the
population-weighted uncertainty map (Figure 3B), the lower
population densities of Madagascar reduced the index on that island
whereas the densely populated Ethiopian highlands remained high.
Plasmodium vivax Endemicity in Central and South East
Large swathes of high endemicity, very large population
densities and a negligible presence of Duffy negativity combine
to make the central and south-eastern regions of Asia by far the
most globally significant for P. vivax. We estimate that India alone
contributed nearly half (46%) of the global population at risk, and
two thirds (67%) of those at stable risk. China is another major
contributor with 19% of the global populations at risk, primarily in
unstable transmission regions, whilst Indonesia and Pakistan
together contributed a further 12%. Within regions of stable
transmission, endemicity is predicted to be extremely heteroge-
neous (Figure 2B). Areas where the point estimate of PvPR1–99
exceeded 7% were found in small pockets of India, Myanmar,
Indonesia, and the Solomon Islands, with the largest such region
located in Papua New Guinea.
The uncertainty map (Figure 3A) reveals how the most precise
predictions were associated with areas of uniformly low endemicity
and abundant surveys, such as Afghanistan and parts of Sumatra
and Kalimantan in Indonesia. Conversely, areas with higher or
more heterogeneous endemicity, such as throughout the island of
New Guinea, were the most uncertain. The population-weighted
uncertainty map (Figure 3B) differs substantively, indicating how
the populous areas of Indonesia, for example, were relatively
precisely predicted whereas India, China, and the Philippines had
the largest per-capita uncertainty.
The status of P. vivax as a major public health threat affecting
the world’s most populous regions is becoming increasingly well
documented. The mantra of vivax malaria being a very rarely
threatening and relatively benign disease [7,10] has been
challenged with evidence suggesting that it can contribute a
significant proportion of severe malaria disease and death
attributable to malaria in some settings . Some reports have
pointed especially to very young children being a major source of
morbidity [20,62] and some hospital-based studies have reported
comparable mortality rates between patients classified with severe
P. vivax and severe P. falciparum [21,24,63]. The recognition of a
lethal threat by this parasite comes with evidence of failing
chemotherapeutics against the acute attack  and overdue
acknowledgement of the practical inadequacy of the only available
therapy against relapse . As the international community
defines increasingly ambitious targets to minimise malaria illness
and death [66–68], and to progressively eliminate the disease from
endemic areas [1–6], further sustained neglect of P. vivax becomes
Here we have presented the first systematic attempt to map the
global distribution of P. vivax endemicity using a defined evidence
base, transparent methodologies, and with measured uncertainty.
These new maps aim to contribute to a more rational international
appraisal of the importance of P. vivax in the broad context of
Table 1. Area and populations at risk of Plasmodium vivax malaria in 2010.
Area (million km2)Population (millions)
Unstable StableAny risk UnstableStableAny risk
America 1.388.08 9.4687.66 49.79 137.45
20.601.86 22.46 48.7237.6686.38
C Asia5.60 3.63 9.24 1,236.92 812.55 2,049.47
SE Asia0.961.78 2.74 150.1764.90 215.07
World 28.55 15.3543.901,523.47964.90 2,488.37
Risk is stratified into unstable risk (PvAPI,0.1 per 1,000 people pa) and stable risk (PvAPI$0.1 per 1,000 people pa).
Global Plasmodium vivax Endemicity in 2010
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malaria control and elimination policies, as well as providing a
practical tool to support control planning at national and sub-
Interpreting P. vivax Endemicity in 2010
In 2010, areas endemic for P. vivax covered a huge geographical
range spanning three major continental zones and extending into
temperate climates. In the Americas, whilst important pockets of
high endemicity are present, the majority of areas of stable
transmission coincide with lower population densities, diminishing
the contribution of this continent to global populations at risk. In
Africa the protection conferred by Duffy negativity to most of the
population means the large swathes of the continent in which
transmission may occur contain only small populations at
biological risk. Thus it is primarily in Asia where very large
populations coincide with extensive high endemic regions, and as a
result nine out of every ten people at risk of P. vivax globally live on
A number of important contrasts arise when comparing this
map with the equivalent 2010 iteration for P. falciparum .
Perhaps most obvious are the lower levels of observed endemicity
at which P. vivax tends to exist within populations experiencing
stable transmission. We used a cartographic scale between 0% and
7% to differentiate global variation in P. vivax endemicity, although
point estimates exceeded that upper threshold in localised areas.
For P. falciparum the equivalent scale spanned 0% to 70% ,
suggesting an approximate order-of-magnitude difference in
prevalence of patent parasitemia. In part, this difference reflects
the decision to standardise our predictions across the 1–99 age
range, and values would have been higher if we had opted for the
peak 2–10 age range used for P. falciparum. This difference might
be accentuated by the likely more rapid acquisition of immunity to
P. vivax than P. falciparum in the most highly endemic areas . A
number of other biological and epidemiological differences
between the two species also mean these lower apparent levels
of endemicity must be interpreted differently. One factor is the
lower sensitivities of microscopy and RDT diagnoses for a given
level of P. vivax infection prevalence, because infections tend to be
associated with much lower parasite densities which increase the
likelihood of false negative diagnoses . A number of studies in
both high and low endemic settings have found microscopy to
underestimate prevalence by a factor of up to three when
compared with molecular diagnosis [57,69–72]. The decreasing
cost and time implications of molecular diagnosis may mean that
these gold standard diagnostic techniques become the standard for
parasite rate surveys in the future. A global map of PCR-positive
parasitemia rates would almost certainly reveal a larger underlying
reservoir of infections and, possibly, reveal systematic differences
in patterns of endemicity than we are able to resolve currently with
less sensitive diagnostic methods.
The lower parasite loads must be interpreted in the context of
implications for progression to clinical disease. For example,
Plasmodium vivax is known to induce fevers at comparatively lower
parasite densities than P. falciparum, a feature likely linked to overall
inflammatory responses of greater magnitude . P. vivax is also
comparable to P. falciparum in its potential to cause anaemia
regardless of lower parasite densities, due to a combination of
dyserythropoesis and repeated bouts of haemolysis . A recent
hospital-based study at a site in eastern Indonesia of hypo- to
meso-endemic transmission of both species showed far lower
frequencies of parasitemia .6,000/uL among inpatients classified
as having not serious, serious, and fatal illness with a diagnosis of P.
vivax compared to P. falciparum . Further, the majority of case
reports describing severe and fatal illness with a diagnosis of vivax
malaria typically show parasitemia .5,000/uL. In contrast, the
World Health Organization threshold for severe illness attribut-
able to hyperparasitemia with P. falciparum is .200,000/uL .
In brief, the relationship between prevalence and risk of disease
and transmission for P. vivax is distinct from that for P. falciparum,
and it is weighted more heavily towards substantial risks at much
lower parasite densities and levels of prevalence of microscopically
The capacity of P. vivax hypnozoites to induce relapsing
infections has a number of important implications. First, because
dormant liver stage infections are not detectable in routine parasite
rate surveys, our maps do not capture the potentially very large
reservoir of asymptomatic infections sequestered in each popula-
tion. Evidence is emerging that this hidden reservoir may be
substantially larger than previously thought, with long-latency P.
vivax phenotypes both prevalent and geographically widespread
. Whilst not contributing to clinical disease until activated,
these dormant hypnozoites ultimately play a vital role in sustaining
transmission since they are refractory to blood-stage antimalarial
chemotherapy and interventions to reduce transmission. Hypno-
zoites also ensure an ability of P. vivax to survive in climatic
conditions that cannot sustain P. falciparum transmission. Second,
the P. vivax parasite rates observed in population surveys detect
both new and relapsing infections, although the two are almost
never distinguishable. This confounds the relationship between
observed infection prevalence and measures of transmission
intensity such as force of infection or the entomological inoculation
rate. This, in turn, has implications for the use of transmission
models seeking to evaluate or optimise control options for P. vivax
[2,9,27,74]. The current unavailability of any diagnostic method
for detecting hypnozoites  and our resulting ignorance about
the size and geographic distribution of this reservoir therefore
remain critical knowledge gaps limiting the feasibility of regional
elimination . It is also worth noting that conventional parasite
rate data do not measure multiplicity of infection which is an
additional potential confounding effect between observed infection
prevalence and transmission intensity.
P. vivax in Africa and Duffy Polymorphism
Our map of P. vivax endemicity and estimates of populations at
risk in Africa are heavily influenced by a single assumption: that
the fraction of the population estimated to be negative for the
Duffy antigen  is refractory to infection with P. vivax. A body of
empirical evidence is growing, however that P. vivax can infect and
cause disease in Duffy negative individuals, as reported in
Madagascar  and mainland sub-Saharan Africa [77–80] as
well as outside Africa [81,82]. Whether the invasion of erythro-
cytes via Duffy antigen-independent pathways is a newly evolved
mechanism, or whether this capacity has been overlooked by the
misdiagnosis of P. vivax in Africa as P. ovale remains unresolved
[9,42,59]. Whilst this accumulated evidence stands contrary to our
simplifying assumption of complete protection in Duffy negative
individuals, there is currently no evidence to suggest that such
infections are anything but rare and thus are unlikely to have any
substantive influence on the epidemiology or infection prevalence
of P. vivax at the population scale throughout most of Africa. We
also make no provision in our model for a protective effect in
Duffy-negative heterozygotes, although such protection has been
observed in some settings [83–86]. The movement and mixing
within Africa of human populations from diverse ethnographic
backgrounds complicates contemporary patterns of Duffy nega-
tivity and, in principle, could yield local populations with
substantially reduced protection from P. vivax infection in the
future. Indeed, the implications for our map of population
Global Plasmodium vivax Endemicity in 2010
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movement go beyond the effect of Duffy negativity: the carriage of
parasites from high to low endemic regions, for example by
migratory workers, may play an important role in sustaining
transmission in some regions and further research is required to
investigate such processes.
Mapping to Guide Control
There exists for P. falciparum a history of control strategies linked
explicitly to defined strata of endemicity, starting with the first
Global Malaria Eradication Programme [18,87,88] and undergo-
ing a series of refinements that now feature in contemporary
control and elimination efforts. Most recently, stratification has
been supported by insights gained from mathematical models
linking endemic levels to optimum intervention suites, control
options, and timelines for elimination planning [2,89–95]. In stark
contrast, control options for P. vivax are rarely differentiated by
endemicity, and there is little consensus around how this may be
done. In part, the absence of agreed control-oriented strata of P.
vivax endemicity stems from the biological complexities and
knowledge gaps that prevent direct interpretation of infection
prevalence as a metric for guiding control. It is also to some extent
inevitable that the dogma of unstratified control becomes self-
propagating: risk maps are not created because control is not
differentiated by endemicity, but that differentiation cannot
proceed without reliable maps.
As well as providing a basis for stratified control and treatment,
the endemicity maps presented here have a number of potential
applications in combination with other related maps. First, there is
an urgent need to better identify regions where high P. vivax
endemicity is coincident with significant population prevalence of
glucose-6-phosphate dehydrogenase deficiency (G6PDd). This
inherited blood disorder plays a key role in chemotherapy policy
for P. vivax because primaquine, the only registered drug active
against the hypnozoite liver stage is contra-indicated in G6PDd
individuals in whom it can cause severe and potentially fatal
haemolytic reactions [96,97]. A new global map of G6PDd
prevalence is now available (Howes et al, submitted) which can be
combined with the endemicity maps presented here to provide a
rational basis for estimating adverse outcomes and setting appro-
priate testing and treatment protocols. Moreover, in practice most
clinical infections are managed without differentiating the causative
parasite species: combining the endemicity maps for P. vivax and P.
falciparum may therefore informunifiedstrategies for malaria control
programs and policy . It has been proposed, for example, that
artemesinin-based combination therapy (ACT) be adopted for all
presumptively diagnosed malaria in areas coendemic for both
species, as opposed to a separate ACT/chloroquine treatment
strategy . Further, in some regions more than 50% of patients
diagnosed with falciparum malaria go on to experience an attack of
vivax malaria in the absence of risk of reinfection . This high
prevalenceof hypnozoites may alsojustifypresumptive therapywith
primaquine against relapse with any diagnosis of malaria where the
two species occur at relatively highfrequencies. Such geographically
specific cross-parasite treatment considerations hinge on robust risk
maps for both species.
Future Challenges in P. vivax Cartography
Numerous research and operational challenges remain unad-
dressed that would provide vital insights into the geographical
distribution of P. vivax and its impacts on populations. Perhaps the
highest priority is to improve understanding of the link between
infection prevalence and clinical burden in both P. vivax mono-
endemic settings and where it is coendemic with P. falciparum.
Official estimates of national and regional disease burdens for P.
vivax remain reliant on routine case reporting of unknown fidelity
and are only crudely distinguished from P. falciparum . It is
illuminating that only 53 of the 95 P. vivax endemic countries were
able to provide vivax-specific routine case reporting data, and
there is a clear mandate for strengthening the routine diagnosis
and reporting of P. vivax cases. Cartographic approaches to
estimating P. vivax burden can therefore play a crucial role in
triangulating with these estimates to provide insight into the
distribution of the disease independent of health system surveil-
lance and its attendant biases [27,101–105]. There is also a
particular need to define burden and clinical outcomes associated
with P. vivax in pregnancy [9,106] and other clinically vulnerable
groups, most notably young children. Linking infection prevalence
to clinical burden implies the need to better understand the
contribution of relapsing infections to disease. Whilst the
magnitude of this contribution is known to be highly heteroge-
neous, its geographical pattern is poorly measured and causal
factors only partially understood [39,41].
Further challenges lie in understanding how P. falciparum and P.
vivax interact within human hosts and how these interactions
manifest at population levels. Comparison of the maps for each
species reveals a complete spectrum from areas endemic for only
one parasite through to others where both species are present at
broadly equal levels. Whilst identifying these patterns of
coendemicity is an important first step, the implications in terms
of risks of coinfection and clinical outcomes, antagonistic
mechanisms leading to elevated severe disease risk, or cross-
protective mechanisms of acquired immunity remain disputed
To meet international targets for reduced malaria illness and
death, and to progress the cause of regional elimination, the
malaria research and control communities can no longer afford to
neglect the impact of P. vivax. Its unique biology and global
ubiquity present challenges to its elimination that greatly surpass
those of its higher-profile cousin, P. falciparum. Making serious
gains against the disease will require substantive strengthening of
the evidence base on almost every aspect of its biology,
epidemiology, control and treatment. The maps presented here
are intended to contribute to this effort. They are all made freely
available from the MAP website  along with regional and
individual maps for every malaria-endemic country. Users can
access individual map images or download the global surfaces for
use in a geographical information system, allowing them to
integrate this work within their own analyses or produce bespoke
data overlays and displays. We will also make available, where
permissions have been obtained, all underlying P. vivax parasite
rate surveys used in this work.
Plasmodium vivax malaria transmission for 2010. S1.1
Overview. S1.2 Identifying Countries Considered P. vivax Malaria
Endemic. S1.3 Updating National Risk Extents with P. vivax
Annual Parasite Incidence Data. S1.4 Biological Masks of
Transmission Exclusion. S1.5 Risk Modulation Based on Medical
Intelligence. S1.6 Assembling the P. vivax Spatial Limits Map. S1.7
Refining Regions of Unstable Transmission after MBG Modelling.
S1.8 Predicting Populations at Risk of P. vivax in 2010.
Updating the global spatial limits of
vivax parasite prevalence database. S2.1 Assembling the
The Malaria Atlas Project Plasmodium
Global Plasmodium vivax Endemicity in 2010
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PvPR Data. S2.2 Database Fidelity Checks. S2.3 Data Exclusions.
S2.4 The PvPR Input Data Set. S2.5 Age-Standardisation. S2.6
work for predicting PvPR1–99. S3.1 Bayesian Inference. S3.2
Model Overview. S3.3 Formal Presentation of Model.
Bayesian model-based geostatistical frame-
results. S4.1 Creation of Validation Sets. S4.2 Procedures for
Testing Model Performance. S4.3 Validation Results.
Model validation procedures and additional
The large global assembly of parasite prevalence data was critically
dependent on the generous contributions of data made by a large number
of people in the malaria research and control communities and these
individuals are listed on the MAP website (http://www.map.ac.uk/
acknowledgements). We thank Professor David Rogers for providing the
Fourier-processed remote sensing data. We are grateful for the comments
of three anonymous referees that have helped strengthen the manuscript.
Conceived and designed the experiments: PWG SIH. Performed the
experiments: PWG APP DLS. Analyzed the data: PWG APP DLS IRFE
CAG KEB. Contributed reagents/materials/analysis tools: IRFE CLM
CAG MFM KEB APP AJT REH DBG PH HFLW RNP IM JKB. Wrote
the paper: PWG.
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Protocol S1 - Updating the global spatial limits of Plasmodium vivax malaria
transmission for 2010
We have previously undertaken an exercise to define the geographical limits of P. vivax
transmission in the year 2009 , stratifying the world into areas considered risk-free or at-risk of
unstable (characterised by annual incidence less than 0.1‰) or stable (annual incidence
exceeding 0.1‰) transmission. The components used to generate these classifications are (i) an
initial identification of those countries housing autochthonous transmission within their borders
(the P. vivax malaria endemic countries, PvMECs); (ii) sub-nationally reported incidence records
from health management information systems (P. vivax annual parasite incidence data, PvAPI);
(iii) additional medical intelligence providing refined risk designations for specific regions such as
islands or cities; (iv) exclusion of risk in areas where the local annual temperature regime cannot
support transmission in an average year; and (v) further exclusion or downgrading of risk in
areas where extreme aridity is likely to limit transmission. For the present study we have
repeated this procedure in full to generate an updated version for 2010. In this supplementary
document we detail the data assembly procedures, how the various components are combined,
and the resulting limits definition.
S1.2 Identifying Countries Considered P. vivax Malaria Endemic
The first version of the P. vivax spatial limits map was developed upon a template consisting
of 95 PvMECs . This list of countries was confirmed as up-to-date for the current 2010
iteration using the same approach and sources of international travel and health guidelines
described previously [1-4].
S1.3 Updating National Risk Extents with P. vivax Annual Parasite Incidence Data
PvAPI Data Processing
The PvAPI data by country were obtained from various sources (Table S1.1). The format in
which these data were made available varied considerably between countries. Ideally, all data
would have been available by administrative unit and by year, with each record representing the
estimated population for the administrative unit and the number of confirmed autochthonous
malaria cases by each human malaria parasite species, which would allow an estimation of
species-specific API. These requirements were sometimes not fulfilled completely and a number
of problems were encountered. First, population data by administrative unit were sometimes
unavailable, in which case these data were sourced separately (using the methods described in
S1.8) or extrapolated from preceding years to estimate PvAPI. Second, not all API data were
species-specific. In these cases, a parasite species ratio was inferred from alternative sources
and applied to provide an estimate of species-specific API. For example, such a ratio was often
available as a single national figure, in which case it was applied uniformly throughout the
country. Third, although a differentiation between microspcopy-confirmed and suspected cases
and between autochthonous and imported cases was often provided, in some cases it had to be
assumed that the data referred to confirmed, autochthonous cases.
PvAPI Data Summaries
Table S1.1 summarizes PvAPI data characteristics for all PvMECs for which these were
available. PvAPI data were not available for countries in the Africa+ region, with the exception of
Djibouti, Namibia, Saudi Arabia, South Africa, Swaziland and Yemen. For Botswana, risk was
constrained to northern districts based upon information from the travel and health guidelines
consulted [3,4]. For other countries in the Africa+ region, stable risk of P. vivax transmission was
assumed to be present throughout their territories. In total, PvAPI data were not available for 42
identified PvMECs, all in Africa+, with the exception of Uzbekistan.
The majority of the PvAPI data were obtained through personal communication with
individuals and institutions linked to national malaria control activities in each country. These are
cited in Table S1.1 and acknowledged on the MAP website
(http://www.map.ox.ac.uk/acknowledgements/). The specific aim was to collate data for the four
most recent years of reporting, ideally including 2010. For six countries the last year of reporting
available was 2010. For three countries, 2009 was the last year of reporting available, whilst
2008 and 2009 were the last years available for 24 and nine countries, respectively. For
Colombia, risk data could not be obtained after 2005. In terms of the length of the period of
reporting, one year of data was available for 14 countries, two years for nine countries, three
years for nine countries and four or more years for 22 countries (Table S1.1).
A total of 18 countries reported at ADMIN1 level and 29 at ADMIN2 level. For southern China,
Myanmar, Nepal and Peru, data were available at ADMIN3 level. In central and northern China
data were available at ADMIN1 level. Data for Namibia and Venezuela were a mixture of
ADMIN1 and ADMIN2 levels. In total, 17,893 administrative units in 53 countries were populated
with PvAPI data (Table S1.1).
Mapping PvAPI Data
In order to map PvAPI data consistently, they were reconciled to the 2009 version of the
Global Administrative Unit Layers (GAUL) data set, implemented by the Food and Agriculture
Organization of the United Nations (FAO) within the EC FAO Food Security for Action
Programme . In some cases this reconciliation was not straightforward given differences in
national sub-divisions. In such cases, alternative sources and maps were used to guide
adequate matching of PvAPI data. For some countries, digital boundary files of the
administrative sub-divisions corresponding to PvAPI data were supplied. These countries were:
Afghanistan, Indonesia, Myanmar, Papua New Guinea, Peru, Solomon Islands, South Africa and
Vietnam. In these cases, coastlines remained the same as the supplied shape files whilst
borders between countries were made congruent with those in the GAUL dataset.
Classification of risk based on PvAPI data was done as described previously .
Classifications of extremely low, unstable transmission of P. vivax were assigned to
administrative units reporting PvAPI of less than 0.1 cases per 1,000 population per annum
(p.a.), and those reporting a PvAPI of ≥ 0.1 cases per 1,000 population p.a. were classified as
having stable transmission.
S1.4 Biological Masks of Transmission Exclusion
We adopt our earlier approach to excluding areas at risk based on environmental non-
suitability for P. vivax transmission . For completeness, we describe in full here the rationale
and methods used.
In some regions, ambient temperature plays a key role in suppressing or precluding P. vivax
transmission via various effects on stages of the parasite and Anopheles vector life cycles - most
importantly by modulating the duration of the extrinsic incubation period of the parasite within the
vector and by affecting daily survival rates of the latter [6-10]. Here, we employ an existing
model  that evaluates temperature effects dynamically through time to generate for each
pixel an index of temperature suitability proportional to vectorial capacity, an established
biological metric of potential transmission intensity [12,13].
In brief, synoptic mean, maximum, and minimum monthly temperature records from 30-
arcsec (~1×1 km) spatial resolution climate surfaces  were converted to a continuous time
series using spline interpolation. This represented the mean temperature profile across an
average year. Diurnal variation  was incorporated by adding a sinusoidal component to the
time series with a wavelength of 24 hours and the amplitude driven by the difference between
the spline-smoothed monthly minimum and maximum values. Ambient temperature can limit or
preclude malaria transmission via a number of influences on components of the transmission
cycle. Although temperature effects have been described on the survival and emergence rates
of mosquito larvae [16,17], and vector feeding rates [18,19], the limiting effects of temperature
on transmission are most pronounced in the interaction between vector lifespan and the duration
of sporogony: the extrinsic incubation period during which the parasite matures into the
sporozoite life stage within the vector. For P. vivax transmission to be biologically feasible, a
cohort of anopheline vectors infected with the parasite must survive long enough for sporogony
to complete within their lifetime. We modelled daily vector survival rate as a continuous function
of local temperature regimes within each pixel using an established relationship drawn from a
series of observational and modelling studies [6-8]. Maximum vector lifespan was defined as 31
days since estimates of the longevity of the main dominant vectors  indicate that 99% of
anopheline vectors die in less than a month. The exceptions were areas that support the longer-
lived Anopheles sergentii and An. superpictus, where 62 days were more appropriate .
Sporogony is also strongly dependent on ambient temperature, so the time required for its
completion varies continuously as temperatures fluctuate across a year . The dependence of
sporogony duration on temperature is classically expressed using a simple temperature-sum
model  in which sporogony occurs after a fixed number of degree-days over a minimum
temperature threshold for development.
The interaction between vector life span and sporogony duration was modelled for each pixel
based on an assumption of constant vector emergence and the continuous evaluation of the
expressions for daily vector survival and accumulation of degree days towards sporogony. A
system of difference equations was implemented that, in effect, simulated the emergence of
successive vector cohorts throughout the year, their declining population size as a function of
temperature, and whether any constituent vectors survived long enough to complete sporogony.
Those pixels in which no window existed across the year for the completion of sporogony were
classified as being at zero risk of transmission. The temperature mask resulting from this
process is shown in Figure S1.1.
A second driver of environmental suitability for P. vivax transmission is the availability of
moisture. We adopted our earlier approach  to mapping those areas where extreme aridity is
likely to prevent transmission by restricting vector survival and availability of oviposition sites
[22,23]. Such areas were identified using pixels defined as 'bare areas' by the GlobCover land-
cover classification product (ESA/ESA GlobCover Project, led by MEDIAS-France/POSTEL)
. GlobCover products are derived from data provided by the Medium Resolution Imaging
Spectrometer (MERIS), on board the European Space Agency’s (ESA) ENVIronmental SATellite
(ENVISAT), for the period between December 2004 and June 2006, and are available at a
spatial resolution of 300 meters . This layer was first resampled to a 1×1 km grid using a
majority filter, and all pixels classified as “bare areas” by GlobCover were overlaid onto the
PvAPI surface. The result is shown in Figure S1.2. The aridity mask was treated differently from
the temperature mask to allow for the possibility of the adaptation of human and vector
populations to arid environments [25,26]. A more conservative approach was taken whereby risk
was down-regulated by one class. In other words, GlobCover’s bare areas defined originally as
at stable risk by PvAPI were stepped down to unstable risk and those classified initially as
unstable were classed as malaria free.
S1.5 Risk Modulation Based on Medical Intelligence
We adopt the same data sources and assembly protocols for incorporation of medical
intelligence information as described previously . For completeness, we detail these data and
methods again here.
Urban areas are less malarious than the surrounding rural environments due to the distinct
ecological conditions presented by man-made environments [27,28]. The extent to which
transmission is reduced will vary according to the local Anopheles species. Urbanisation has
been shown to reduce malaria transmission, measured by the entomological inoculation rate, by
an order of magnitude across Africa, due to reduced vector diversity and density, as well as
lower anopheline survival, biting and sporozoite rates in urban versus rural areas .
Anopheles darlingi, the main malaria vector in America, has shown itself to be similarly unsuited
to urban environments .
Urban malaria transmission is more entrenched in the Indian subcontinent because of the
presence of An. stephensi and, to a lesser extent, An. culicifacies, both recognised urban
malaria vectors . No malaria vector is better adapted to urban environments than An.
stephensi, and this is due to its ability to breed in all types of artificial collections of water, such
as wells, pits, tanks and drains . Anopheles culicifacies is less resilient to man-made
environments and is particularly affected by pollution of water sources [32,33]. Importantly, the
vector densities and sporozoite rates of both these species have been shown to decrease from
peri-urban to urban areas [32,34,35]. Despite this decrease, it is estimated that approximately
8% of reported malaria cases in India come from urban areas , with incidence often
surpassing the stable risk threshold. Reported annual parasite incidence (API) estimates
amongst 86 cities across India in 1993 ranged from 0 to 51.85 cases per 1,000 people per
annum (p.a.), with a median of 0.97 . Seventy of these cities would have been classified as
supporting stable transmission according to the API threshold used in this paper (i.e. API ≥0.1
case per 1,000 people p.a.). In 2004, the API in an impoverished area located in the outskirts of
Kolkata was measured at 1.5 cases per 1,000 residents p.a., with the majority (97%) due to P.
vivax . Since An. culicifacies seems to be more affected by the process of urbanisation, it
was assumed that urban malaria transmission is maintained mainly by An. stephensi (Figure
S1.4) as defined by the rules of risk modulation described below.
Risk Modulation in Specified Urban Areas
There are 59 cities cited as being malaria free in the two sets of international travel and health
guidelines consulted [39,40] (Table 1). In addition, urban areas in China, the Philippines and
Indonesia (specifically those located in Sumatra, Kalimantan, Nusa Tenggara Barat and
Sulawesi) are said to be malaria free. This is obviously not a comprehensive list of malaria free
cities but rather one restricted to main destinations of interest to travellers. Specific cities were
geo-positioned and their urban extents were identified using the Global Rural Urban Mapping
Project (GRUMP) urban extents layer . In China, the Philippines and specified areas of
Indonesia all urban extents were identified and mapped. The resulting layer was overlaid on the
PvAPI layer and biological masks to identify the underlying risk of malaria. Those cities falling
within the range of An. stephensi  were also identified.
Of the 59 specified cities, 17 are in areas where P. vivax malaria transmission is absent as
defined by the PvAPI layer and the biological masks (e.g. highland areas). The urban extents of
the remaining 42 cities cover areas defined as unstable or stable transmission or both (Table
S1.2). Only eight of these cities fall within the range of An. stephensi: six in India (Bangalore,
Kolkata, Mumbai, Nagpur, Nashik and Pune) and two in Myanmar (Mandalay and Yangon;
Figure). In addition, cities in south-western Yunnan, China, also fall in areas inhabited by this
For all cities falling outside the range of An. stephensi, risk was classified as absent
throughout their urban extents. For those cities falling within the range of An. stephensi, risk was
assumed to be one level lower than the surrounding risk defined by PvAPI data and the
biological masks. This is to allow for the potential transmission of malaria by An. stephensi
combined with the transmission reducing effects of urban areas [32,34,35].
Risk Exclusion in Administrative Areas
Some sub-national administrative areas and territories are listed as being malaria free by the
international travel and health guidelines consulted [39,40]. These are shown in Table 2. Such
territories were mapped using the GAUL data set  and risk within them was assigned a
malaria free category, if not already classified as such by the PvAPI layer and the biological
masks. In addition to the territories listed in Table S1.3, the island of Socotra, in Yemen, has not
reported cases since 2005 after malaria elimination activities were initiated in 2000 ; this
island was assumed to be malaria free. Two further exclusions were those of the island of
Aneityum, in Vanuatu , and the Angkor Watt area, in Cambodia (corresponding to two
districts in Siem Reap province), which were classified as malaria free following personal
communication with malaria experts in these countries.
S1.6 Assembling the P. vivax Spatial Limits Map
Figure S1.3 summarises the different steps undertaken to assemble the P. vivax spatial limits
map. The various data sources described above were progressively applied on a geographical
information system with subsequent reductions in estimated area and population at risk. This
sequence is illustrated as different maps in Figure S1.5A-E
S1.7 Refining Regions of Unstable Transmission after MBG Modelling
In some regions within the estimated limits of stable transmission, the model-based
geostatistical predictions of PvPR1-99 were extremely low, either because of a dense abundance
of survey data reporting zero infections or, in Africa, because of very high coincident Duffy
negativity phenotype frequencies suppressing prevalence predictions. Such areas are not
appropriately described as at stable risk and so we defined a decision rule whereby pixels
predicted with high certainty (probability >0.9) of being less than 1% PvPR1-99 were identified and
assigned to the unstable class, thereby modifying the original transmission limits (Figure S1.5F).
These augmented mapped limits were combined with a 2010 population surface derived from
the Global Rural Urban Mapping Project (GRUMP) beta version [41,46] to estimate the number
of people living at no risk or at unstable or stable risk within each country and region.
S1.8 Predicting Populations at Risk of P. vivax in 2010
The Global Rural Urban Mapping Project (GRUMP) beta version provides gridded population
counts and population density estimates at 1×1 km spatial resolution for the years 1990, 1995
and 2000, both adjusted and unadjusted to the United Nations’ national population estimates
[41,46]. The adjusted population counts for the year 2000 were projected to 2010 by applying
the relevant urban and rural national growth rates by country  using methods described
previously . The urban growth rates were applied to populations residing within the GRUMP-
defined urban extents , and the rural rates were applied elsewhere. National 2010 totals
were then adjusted to match those estimated by the United Nations .
The population grid was overlaid with the categorised limits map and the total population located
within each limits class was computed. An equivalent calculation was made of the land area
associated with each class, by replacing the population grid with one quantifying the surface
area of each pixel, taking into account the equirectangular map projection used. Additionally, the
population grid was combined with the uncertainty maps to provide a population-weighted index
of uncertainty (the product of the log of population density and the reciprocal of the probability of
correct class assignment).
Table S1.1. Summary of the P. vivax annual parasite incidence (PvAPI) data assembled for
each country. The data are grouped by the three global regions defined by Hay et al. :
Africa+, America and Central and South East (CSE) Asia. ADMIN1, 2 or 3 refers to the
administrative division level (first, second or third level) at which data were available. The
number of risk units refers to how many administrative units, at the level specified, were
populated with actual data. Year start and Year end mark the start and end of the period for
which data were available.
Region Country Admin. level
Year start Year end Source
Africa+ Djibouti ADMIN1 5 2007 2009 
Africa+ Namibia ADMIN1 & ADMIN2 30 2009 2009 
Africa+ Saudi Arabia ADMIN1 13 2005 2006 
Africa+ South Africa ADMIN2 257 2006 2009 
Africa+ Swaziland ADMIN2 53 2007 2009 
Africa+ Yemen ADMIN1 19 2002 2006 
America Argentina ADMIN2 513 2008 2008 
America Belize ADMIN1 6 2006 2006 
America Bolivia ADMIN2 113 2008 2008 
America Brazil ADMIN2 5510 2004 2008 
America Colombia ADMIN2 1087 2005 2005 
America Costa Rica ADMIN2 81 2006 2006 
America Ecuador ADMIN2 220 2005 2008 
America El Salvador ADMIN1 14 2006 2006
America French Guiana ADMIN2 21 2006 2006
America Guatemala ADMIN1 22 2006 2006 
America Guyana ADMIN1 10 2004 2007
America Honduras ADMIN2 291 2005 2008 
America Mexico ADMIN2 2454 2005 2008 
America Nicaragua ADMIN1 17 2004 2007 
America Panama ADMIN2 68 2006 2007 
America Paraguay ADMIN2 219 2008 2008 
America Peru ADMIN3 1828 2005 2008 
America Suriname ADMIN1 10 2008 2008 
America Venezuela ADMIN1 & ADMIN2 30 2004 2008 
CSE Asia Afghanistan ADMIN2 398 2005 2008 
CSE Asia Azerbaijan ADMIN1 73 2005 2008 
CSE Asia Bangladesh ADMIN2 64 2007 2008 
CSE Asia Bhutan ADMIN1
20 2004 2010 [76,77]
CSE Asia Cambodia ADMIN1 26 2005 2008 
CSE Asia China ADMIN1 & ADMIN3 263 2003 2007 
CSE Asia Georgia ADMIN2 79 2005 2010 [80,81]
Region Country Admin. level
Year start Year end Source
CSE Asia India ADMIN2 574 2004 2007 
CSE Asia Indonesia ADMIN2 346 2005 2008 
CSE Asia Iran ADMIN2 283 2007 2008 
CSE Asia Iraq ADMIN2 11 2005 2008 
CSE Asia Lao PDR ADMIN2 139 2006 2008 
CSE Asia Kyrgyzstan ADMIN1 1 2008 2008 [3,4]
CSE Asia Malaysia ADMIN1 15 2003 2010 [85,86]
CSE Asia Myanmar ADMIN3 325 2006 2008 
CSE Asia Nepal ADMIN3 75 2005 2010 [88,89]
CSE Asia Pakistan ADMIN2 119 2005 2008 
CSE Asia Papua New Guinea ADMIN2 87 2005 2007 
CSE Asia Philippines ADMIN2 82 2004 2007 
CSE Asia Rep. of Korea ADMIN2 239 2005 2008 
CSE Asia Solomon Islands ADMIN1 10 2003 2007 
CSE Asia Sri Lanka ADMIN2 25 2006 2010 
CSE Asia Tajikistan ADMIN2 56 2005 2008 
CSE Asia Thailand ADMIN1 76 2006 2010 
CSE Asia Timor-Leste ADMIN1 13 2008 2008 
CSE Asia Turkey ADMIN2 926 2008 2008 
CSE Asia Vanuatu ADMIN1 6 2003 2007 
Viet Nam ADMIN2 671 2005 2008 
Table S1.2 Cities cited as being malaria free by the sources consulted [39,40]. Defined risk
refers to the malaria risk categories defined by the PvAPI layer and biological masks; note that
urban extents often cover more than one category. Modified risk refers to the new malaria risk
categories assigned according to the rules described in the text. Cities where the defined risk
was “free” were not affected by these rules.
Country City Defined risk Modified risk
Azerbaijan Baku Free, unstable Free
Bangladesh Dhaka Free -
Belize Belize Unstable Free
Bolivia La Paz Free -
Botswana Gaborone Free -
Cambodia Phnom Penh Free, unstable Free
Colombia Bogota Free, unstable Free
Colombia Cartagena Free, unstable Free
Costa Rica Puerto Limon Unstable Free
Ecuador Guayaquil Unstable, stable Free
Ecuador Quito Free -
Eritrea Asmara Stable Free
Ethiopia Addis Ababa Stable, free Free
French Guiana Cayenne Free -
Georgia Tblisi Unstable, free Free
Guatemala Antigua Free -
Guatemala Guatemala Free -
Honduras San Pedro Sula Unstable Free
Honduras Tegucigalpa Unstable, free Free
India Bangalore Stable Unstable
India Kolkata Unstable, stable Free, unstable
India Mumbai Stable, unstable Unstable, free
India Nagpur Stable Unstable
India Nasik Unstable Free
India Pune Unstable Free
Indonesia Jakarta Free -
Iraq Baghdad Free -
Iraq Ramadi Free -
Iraq Tikrit Free -
Kenya Nairobi Stable Free
Kyrgyzstan Bishkek Free -
Laos Vientiane Free, unstable Free
Myanmar Mandalay Free, stable, unstable Free, unstable
Myanmar Yangon Unstable Free
Nepal Kathmandu Free -
Nicaragua Managua Unstable Free
Panama Panama Unstable Free
Peru Cuzco Free -
Saudi Arabia Jeddah Unstable Free
Saudi Arabia Mecca Unstable Free
Saudi Arabia Medina Unstable Free
Saudi Arabia Riyadh Free -
Saudi Arabia Ta'if Unstable Free
Suriname Paramaribo Free -
Thailand Bangkok Free, unstable Free
Thailand Chiang Mai Stable Free
Thailand Chiang Rai Unstable Free
Thailand Koh Phangan Stable Free
Thailand Koh Samui Stable Free
Thailand Pattaya Unstable Free
Viet Nam Can Tho Free, unstable Free
Viet Nam Da Nang Unstable Free
Viet Nam Haiphong Free -
Viet Nam Hanoi Free, unstable Free
Viet Nam Ho Chi Minh City Unstable Free
Viet Nam Hue Free, unstable Free
Viet Nam Nha Trang Free, unstable Free
Viet Nam Qui Nhon Unstable Free
Yemen Sana’a Unstable Free
Table S1.3. Administrative areas defined as being malaria free by international travel and health
Administrative areas/sub-national territories
French Guiana Devil's Island
Mauritania Adrar, Dakhlet-Nouadhibou, Inchiri and Tiris-Zemmour regions
Aklan, Albay, Benguet, Bilaran, Bohol, Camiguin, Capiz, Catanduanes, Cavite,
Cebu, Guimaras, Iloilo, Northern Leyte, Southern Leyte, Marinduque, Masbate,
Eastern Samar, Northern Samar, Western Samar, Sequijor, Sorsogon, Surigao
Del Norte and metropolitan Manila
Sri Lanka Colombo, Galle, Gampaha, Kalutara, Matara, and Nuwara Eliya
Venezuela Margarita Island (Nueva Esparta)
Figure S1.1. Environmental suitability for transmission of P. vivax as defined by
temperature. Areas shaded green are those in which no windows exist across an average year
in which the annual temperature regime is likely to support the presence of infectious vectors.
The temperature suitability model is described in full elsewhere .
Figure S1.2. Environmental suitability for transmission of P. vivax as defined by extreme
aridity. Areas shaded grey are those classified as bare areas by the GlobCover land cover
product, interpreted as lacking sufficient moisture to support populations of Anopheles
necessary for transmission.
Figure S1.3. Flow chart of the various exclusion layers used to derive the final map. Area
(expressed in km2) and population at risk (PAR; expressed in millions) excluded are shown at
each step to illustrate how these were reduced progressively.
Figure S1.4. Distribution of An. stephensi  (hatched area) and location of the eight cities
falling within this range (light blue circles) overlaid on the PvAPI/biological masks-defined P.
vivax limits of transmission.
Figure S1.5. Map sequence illustrating the different exclusion layers applied. A = all
regions of the 95 P. vivax endemic countries; B = downgrading or exclusion of risk informed by
annual parasite incidence data (PvAPI); C = additional exclusion of risk informed by the
biological temperature mask; D = additional downgrading or exclusion of risk informed by the
aridity mask; E = additional downgrading or exclusion of risk informed by medical intelligence
and international travel and health guidelines; F = the final limits definition after additionally
downgrading risk in stable areas predicted to have very low prevalence by the MBG model.
Stable transmission is shown in red, unstable transmission in pink and P. vivax malaria free
areas in grey.
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Protocol S2: The Malaria Atlas Project Plasmodium vivax parasite prevalence
The Malaria Atlas Project (MAP) database includes estimates of Plasmodium falciparum and
P. vivax parasite prevalence derived from random community surveys covering the period 1985
to 2010 . This document describes the P. vivax parasite rate (PvPR) data assembly, auditing
steps performed on this database, the exclusions applied prior to modelling and some key
features of the PvPR data set used in the global P. vivax endemicity models described in this
S2.1 Assembling the PvPR Data
The PvPR data inclusion criteria, search strategy, extraction and geo-positioning have been
described in detail elsewhere . They are reviewed briefly in the following paragraphs.
PvPR Data Inclusion Criteria
The inclusion criteria of the MAP PvPR database are listed in Table S2.1. Two of these have
been modified since the original protocols set out at the onset of MAP . First, the original
inclusion criterion of a minimum sample size of ≥50 individuals surveyed was removed because
the endemicity models adjust for sample size. A total of 2,243 small sample surveys (n<50)
would have been otherwise excluded without this revision. Second, the 36 months duration
interval between surveys conducted at the same location (spatial duplicates) was relaxed to
Data Search Strategies
Data searches aimed at the retrieval of data from published and unpublished sources for as
many P. vivax malaria endemic countries (PvMECs) as possible. The published scientific
literature was scanned weekly for data through subscription to malaria publications newsletters
(mainly Malaria World newsletters (http://www.malaria-world.com/) and the Environmental
Health at USAID malaria bulletins (http://www.ehproject.org/)). This was complemented by
periodic data searches in online reference archives (mainly PubMed
(http://www.ncbi.nlm.nih.gov/sites/entrez), ISI Web of Knowledge (http://wok.mimas.ac.uk) and
Scopus (http://www.scopus.com)) to ensure that all relevant publications were captured.
Keywords used in these searches were “malaria” and “[Country Name]”. Data from unpublished
sources were mainly obtained through personal communication with malaria specialists. Full
acknowledgement of these interactions and data provision is provided on the MAP website
Data Abstraction and Entry
The minimum required data fields for each record were: description of the study area (name,
administrative divisions and geographical coordinates, if available), the dates of start and end of
the survey (month and year) and information about blood examination (number of individuals
tested, number positive for P. vivax and examination method). Authors were contacted to
request any missing information. Some survey results were reported as an aggregate in space
(e.g. a single PvPR for a group of villages) or time (e.g. a mean PvPR estimated from four
different surveys conducted in a period of time). In such cases, authors were contacted to
attempt to obtain disaggregated data (e.g. a PvPR estimate per village or per each of the cross-
sectional surveys conducted in a time period). Data were entered in a PostgreSQL 8.3 database
(PostgreSQL Global Development Group, 2009) running on a Unix platform.
Data geo-positioning was a particularly demanding task during data entry. The same
guidelines described previously  were used here. According to their spatial representation,
data were classified as points (corresponding to an area ≤10 km2), wide-areas (>10 and ≤25
km2), small polygons (>25 and ≤100 km2) or large polygons (>100 km2). Authors were contacted
when reporting results as polygon data to try to disaggregate them into points or wide-areas.
Various digital resources were used to geo-position data, amongst which the most useful were
Microsoft Encarta Encyclopedia (Microsoft, 2004) and Google Earth (Google, 2009). Importantly,
the provision of GPS readings accompanying raw data obtained through personal
communication both decreased the burden of this task and improved the precision of data geo-
S2.2 Database Fidelity Checks
The entire database was first checked with a series of simple range-check constraint queries
to identify potential errors that could have occurred during data entry. These queries assessed
all data fields relevant to modelling for missing or inconsistent information. The fields checked
included those describing the study area (area type, geographical coordinates, and urban or
rural author definitions) and those providing specific information about the survey (month and
year of start and end of the survey, age range of study population, number examined and
positive for P. vivax, and diagnostic method utilised).
The second objective was to check that survey sites were located precisely with respect to
the master raster grid templates in which the endemicity models were developed. The locations
therefore needed to be on grid squares identified as land and within the border of the country in
which the survey was conducted. All survey locations were intersected with the relevant grids
and erroneous locations were identified and corrected manually. Typically this occurred in areas
of complex coastlines with an average displacement of <1 km.
The final objective was to check for any duplicates introduced during the iterative data
assembly process. Pairs of survey sites found within 1 km were listed and further checks
identified whether they corresponded to the same location.
S2.3 Data Exclusions
The database was subjected to exclusions in order to attempt optimal spatial and temporal
resolution of the data prior to modelling. Large (n=43) and small polygon data (n=41) were
excluded because these records represented areas larger than the 5 × 5 km spatial resolution
grid output of the model. In total, 226 records were excluded for the reasons outlined. Table S2.2
and Figure S2.1 summarise these exclusions by region. Spatiotemporal duplicates (i.e. those
conducted at the same location with less than three months gap between surveys) were
excluded unless it could be shown that different communities were sampled by the different
surveys (e.g. separate but neighbouring schools).
S2.4 The PvPR Input Data Set
The data exclusions outlined above resulted in the PvPR input data set for the geo-statistical
models. Some summary figures describing this data set are presented in Table S2.3 and are
further discussed below.
On 25th November 2011, after all checks were performed, the database was considered
ready for the endemicity models. PvPR data were available for 52 countries (12 in America, 18
in Africa+ and 22 in CSE Asia) (Table S2.2). A total of 43 PvMECs, mostly in Africa+, were not
represented in the database. This explains the significantly lower number of PvPR data records
relative to the MAP PfPR database . The PvMECs outside Africa+ not represented in the
database were Argentina, Azerbaijan, Belize, Bhutan, El Salvador, Georgia, Guyana, Iran, Korea
DPR, Kyrgyzstan, Panama, Paraguay, Republic of Korea and Uzbekistan. The five data richest
countries were Indonesia (n=4,457), Ethiopia (n=826), Viet Nam (n=657), Afghanistan (n=493)
and Bangladesh (n=365). The PvPR data were extracted from 432 different sources.
Total Number of Records
A total of 9,970 PvPR estimates between 1985 and 2010 were available for modelling (388 in
America, 1,640 in Africa+ and 7,942 in CSE Asia; Figures S2.1 and S2.2). There was a lack of
PvPR data from central, western and eastern Africa and 86% of the data from Africa+ derived
from large surveys in three countries: Ethiopia, Sudan and Zambia. In CSE Asia, 56% of the
data were from surveys done in one country, Indonesia. In the Americas, the majority of the data
(45%) were from Brazil.
Summary of Recorded PvPR Estimates
The recorded PvPR values were generally low. Globally, more than half (51%) of the PvPR
surveys reported zero prevalence. In Africa+, 79% of the data were absence records, compared
to 44% in America and 46% in CSE Asia. In Africa+, median and mean PvPR were zero and
0.01, respectively. The PvPR in America had a median of 0.01 and a mean of 0.03 and in CSE
Asia the median was 0.01 and the mean 0.04 (Table S2.3).
Data and Geographic Coordinate Sources
The great majority of the PvPR data (81%) were obtained from sources that had not been
peer-reviewed, including reports and theses, with only 10% contributed by the peer-reviewed
scientific literature. Over one-third (36%) of the geographical coordinates were obtained from
personal communication, mostly in the form of GPS readings. Geo-positioning was particularly
difficult in CSE Asia, where more than a third of the survey sites were geo-positioned using a
combination of methods to overcome these difficulties.
Year of Survey
Most of the data assembled derived from surveys conducted from the year 2000 onwards
(82%) and from 2005 (66%) (Figure S2.2). Nearly one third of the data (33%) was collected in
the years 2007 and 2008, the latter representing the data richest year. The majority of surveys in
Africa+ and CSE Asia were conducted in the period between 2005 and 2010 (76% and 66%,
Age Range of Study Populations
In Table S2.3 the PvPR data are summarised into four groups according to the upper age
limit of the study population. The great majority of survey samples (88%) included adults (>20
years of age). This was particularly true for America and CSE Asia, where 97 and 91% of
surveys, respectively, sampled this age group. In Africa+ adults were sampled in 70% of
Malaria prevalence surveys using only the two most common diagnostic methods,
microscopy and rapid diagnostic tests (RDTs), were incorporated into the PvPR database. All
surveys from America and Africa+ used microscopy as the examination method. In CSE Asia
2,314 surveys in 2007-2010 used RDTs despite the relative limitation of current tests for
diagnosing P. vivax infection . Only 399 of those documented used an RDT that targets P.
vivax specifically (Table S2.4).
Survey Sample Sizes
Sample sizes ranged from one to more than 15,000 individuals per survey. Median sample
sizes were 92, 47 and 120 for America, Africa+ and CSE Asia, respectively. Globally, the
median sample size was 107 (IQR: 54-246). A total of 112 surveys did not report the number of
individuals tested. In these cases, and since the models required a sample size to be recorded,
the latter was inferred from additional information provided by the source or assumed to be 50 if
no such information was available.
The PvPR data were reported in a diversity of age ranges. Like P. falciparum, population
measures of P. vivax malaria prevalence are age-dependent . It was therefore necessary to
standardise the PvPR survey estimates to a single, representative age group for comparison. All
surveys were standardised to the 1 to 99 year age group (PvPR1-99) using the same underlying
model form implemented previously for P. falciparum  based on catalytic conversion models
first adapted to malaria by Pull and Grab  and described in detail elsewhere .
Sixty-seven studies were used to develop the age-standardization module of the model,
which included 1,696 records of disaggregated age data from 67 unique sites. Sixty of the
studies [6-9] were conducted in Indonesia and seven in Papua New Guinea [10-21]. The number
of age strata varied among studies, but ranged from seven to 64 (mean = 25).
As in the most recent study to map P. falciparum globally , we adopt here a regional
modelling strategy whereby separate and independent models are fitted to regional subsets of
the global database. The rationale for stratifying the modelling geographically is two-fold. First,
the computational resources required to fit the model (i.e. to estimate parameter distributions via
Markov chain Monte Carlo (MCMC)) and use the fitted model to generate predictive maps are
heavily dependent on the number of data points being considered. The required computational
memory (RAM) and processing (CPU time) tend to scale cubically with the number of data.
There are also sound statistical reasons for geographic stratification because each regional
model is able to fit parameter distributions independently of those in other regions. This has the
practical advantage that systematic differences in the spatial heterogeneity of endemicity
between regions, or in the relationship between endemicity and environmental covariates can be
better represented with regionally bespoke models. In statistical parlance, this feature allows
parameter non-stationarity to be captured . Weighed against these advantages is the issue
of data availability. Clearly, if a spatial data set is divided into too many spatial regions, or the
regions are inappropriately defined, it may mean that some regions have insufficient data with
which to fit robust models.
We subdivided the 95 PvMECs globally into four regions, as shown in Figure S2.3. The sizes
of the regions were chosen to strike a balance between too little data, which would yield
unacceptable levels of uncertainty, and too much data, which would yield unacceptable
computational cost. An immediate disadvantage with regional stratifications is the potential for
marked discontinuities in predictions along the boundaries when regions are re-joined to make a
final global map. Such discontinuities are biologically implausible, as well as being aesthetically
unwelcome in presented maps. To mitigate this effect, the stratified data sets were defined so
that each region drew information from data both within the region and within a buffer of one
decimal degree (approximately 111km at the equator) around the region’s boundary. This had
the practical effect of drawing the levels of predicted surfaces from neighbouring regions to
within similar ranges around border regions, reducing the potential for discontinuity.
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Table S2.1. The inclusion criteria for the MAP PvPR database.
Revised for PvPR database
Time of survey Post 1984 No change
Sampling method Random, community based No change
Intervention studies Pre-intervention only No change
Spatial duplicate time window >36 months >3 months
Numerator/denominator Required No change
Age groups sampled Children preferred (Africa) No change
Spatial coverage Points/wide-areas preferred No change
Microscopy preferred over RDT No change
Table S2.2. Exclusions applied to the PvPR database.
Countries with PvPR survey data†
Total records in starting database
Surveys with missing month
Total records for input data set
†Those countries from which PvPR survey data were available are listed alphabetically by region: America (Bolivia, Brazil, Colombia,
Costa Rica, Ecuador, French Guiana, Honduras, Mexico, Nicaragua, Peru, Suriname and Venezuela); Africa+ (Cameroon,
Equatorial Guinea, Ethiopia, Ghana, Kenya, Madagascar, Mali, Namibia, Nigeria, Sao Tome and Principe, Saudi Arabia, Senegal,
Sierra Leone, Somalia, South Sudan, Sudan, Yemen and Zambia); and CSE Asia (Afghanistan, Bangladesh, Cambodia, China,
India, Indonesia, Iraq, Lao People's Democratic Republic, Malaysia, Myanmar, Nepal, Pakistan, Papua New Guinea, Philippines,
Solomon Islands, Sri Lanka, Tajikistan, Thailand, Timor-Leste, Turkey, Vanuatu and Viet Nam).
Table S2.3. Summary of the most important aspects of the PvPR data by region.
Total records of input data set
Number of zero records
Primary source of PvPR data
Peer reviewed sources
Source of spatial coordinates
Other digital gazetteers
Time period of surveys
Upper age sampled
>10 and <=15
>15 and <=20
RDT – slide confirmed
†Ministry of Health reports, theses and other unpublished sources.
120 (54-281) 107 (54-246)
Table S2.4. RDTs used as examination method by surveys in CSE Asia.
RDT name Number of records Target species*
CareStart Malaria 28
ICT Malaria Pf/Pv 36
Not specified 1,935
*Pf = P. falciparum; Pv = P. vivax, Pan = Plasmodium species, NA = not applicable. ‡The specific type of CareStart Malaria test was
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Figure S2.1. Sequence of data exclusion rules for the formulation of a refined global PvPR
input data set for modelling. For each stage of exclusion the number of records excluded are
shown in parentheses.