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Estimating the Global Clinical Burden of Plasmodiumfalciparum Malaria in 2007Simon I. Hay1,2*, Emelda A. Okiro2,3, Peter W. Gething1, Anand P. Patil1, Andrew J. Tatem4,5, Carlos A.

Guerra1, Robert W. Snow2,3*

1 Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom, 2 Malaria Public Health and Epidemiology Group,

Centre for Geographic Medicine, KEMRI–University of Oxford–Wellcome Trust Research Programme, Nairobi, Kenya, 3 Centre for Tropical Medicine, Nuffield Department of

Clinical Medicine, University of Oxford, Oxford, United Kingdom, 4 Department of Geography, University of Florida, Gainesville, Florida, United States of America,

5 Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America

Abstract

Background: The epidemiology of malaria makes surveillance-based methods of estimating its disease burden problematic.Cartographic approaches have provided alternative malaria burden estimates, but there remains widespreadmisunderstanding about their derivation and fidelity. The aims of this study are to present a new cartographic techniqueand its application for deriving global clinical burden estimates of Plasmodium falciparum malaria for 2007, and to comparethese estimates and their likely precision with those derived under existing surveillance-based approaches.

Methods and Findings: In seven of the 87 countries endemic for P. falciparum malaria, the health reporting infrastructurewas deemed sufficiently rigorous for case reports to be used verbatim. In the remaining countries, the mapped extent ofunstable and stable P. falciparum malaria transmission was first determined. Estimates of the plausible incidence range ofclinical cases were then calculated within the spatial limits of unstable transmission. A modelled relationship betweenclinical incidence and prevalence was used, together with new maps of P. falciparum malaria endemicity, to estimateincidence in areas of stable transmission, and geostatistical joint simulation was used to quantify uncertainty in theseestimates at national, regional, and global scales. Combining these estimates for all areas of transmission risk resulted in 451million (95% credible interval 349–552 million) clinical cases of P. falciparum malaria in 2007. Almost all of this burden ofmorbidity occurred in areas of stable transmission. More than half of all estimated P. falciparum clinical cases and associateduncertainty occurred in India, Nigeria, the Democratic Republic of the Congo (DRC), and Myanmar (Burma), where 1.405billion people are at risk. Recent surveillance-based methods of burden estimation were then reviewed and discrepancies innational estimates explored. When these cartographically derived national estimates were ranked according to their relativeuncertainty and replaced by surveillance-based estimates in the least certain half, 98% of the global clinical burdencontinued to be estimated by cartographic techniques.

Conclusions and Significance: Cartographic approaches to burden estimation provide a globally consistent measure ofmalaria morbidity of known fidelity, and they represent the only plausible method in those malaria-endemic countries withnonfunctional national surveillance. Unacceptable uncertainty in the clinical burden of malaria in only four countriesconfounds our ability to evaluate needs and monitor progress toward international targets for malaria control at the globalscale. National prevalence surveys in each nation would reduce this uncertainty profoundly. Opportunities for furtherreducing uncertainty in clinical burden estimates by hybridizing alternative burden estimation procedures are alsoevaluated.

Please see later in the article for the Editors’ Summary.

Citation: Hay SI, Okiro EA, Gething PW, Patil AP, Tatem AJ, et al. (2010) Estimating the Global Clinical Burden of Plasmodium falciparum Malaria in 2007. PLoSMed 7(6): e1000290. doi:10.1371/journal.pmed.1000290

Academic Editor: Ivo Mueller, Papua New Guinea Institute of Medical Research, Papua New Guinea

Received February 15, 2010; Accepted May 5, 2010; Published June 15, 2010

Copyright: � 2010 Hay et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricteduse, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: SIH is funded by a Senior Research Fellowship from the Wellcome Trust (#079091), which also supports CAG, PWG and, previously, AJT. AJT is nowsupported by a grant from the Bill & Melinda Gates Foundation (#49446). EAO is supported by the Wellcome Trust under their Research Training Fellowshipprogramme (#086166). RWS is a recipient of a Wellcome Trust Principal Research Fellowship (#079080), which also supports APP. We are also grateful to AmazonWeb Services for providing a research grant that facilitated the use of their Elastic Compute Cloud facility (http://aws.amazon.com/ec2) for the large-scalecomputations in this study. This work forms part of the output of the Malaria Atlas Project (MAP, http://www.map.ox.ac.uk), principally funded by the WellcomeTrust (UK). 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.

Abbreviations: Africa+, Africa, Yemen, and Saudi Arabia; AFRO, African Regional Office; AMRO, American Regional Office; CSE Asia, Central and South East Asia;DRC, Democratic Republic of the Congo; EMRO, Eastern Mediterranean Regional Office; EURO, European Regional Office; GRUMP, Global Rural-Urban MappingProject; HMIS, health management information systems; MAP, Malaria Atlas Project; PA, per annum; PAR, population at risk; PHC, primary health centre; PfAPI, P.falciparum annual parasite incidence; PfMEC, P. falciparum malaria endemic country; PfPR, P. falciparum parasite rate; SEARO, South East Asian Regional Office;WHO, World Health Organization; WPRO, Western Pacific Regional Office.

* E-mail: [email protected] (SIH); [email protected] (RWS)

PLoS Medicine | www.plosmedicine.org 1 June 2010 | Volume 7 | Issue 6 | e1000290

Introduction

Estimating the disease burden posed by malaria is an

important public health challenge [1–9]. The clinical conse-

quences of Plasmodium falciparum infection have several features

that confound traditional approaches to disease burden and

disability measurement [10,11]. First, not all infections result in

progression to disease, notably in areas of stable transmission

[12], where populations have acquired clinical immunity [13].

The overall risk of clinical disease has a curvilinear and

uncertain association with the risk of infection as a combined

function of age at first infection and immunity [13–18]. Second,

the dominant symptom of fever, or other symptoms, does not

distinguish malaria from other locally prevalent infections

[19–23]. As a consequence, the routine reporting of ‘‘malaria’’

can overestimate disease rates by assuming that most fevers are

malaria [24,25] and that fevers associated with an infection are

causally linked to that infection [20,26]. Third, with few

exceptions across malaria-endemic countries, fevers or other

malaria-like syndromes are often self-medicated and may

resolve regardless of cause before reaching formal health

systems [27]. Fourth, inaccurate diagnoses [21,25,28] might

be used to report disease rates, and these errors may be

compounded through inadequate and incomplete national

reporting systems [29–38].

To circumvent some of the clinical, treatment, and reporting

problems inherent in malaria burden estimation, we previously

computed the global incidence of P. falciparum clinical disease [5]

for 2002, using assemblies of epidemiological data and a modified

categorical map of historical malaria endemicity [39]. The

publication of (i) the revised global spatial limits of P. falciparum

transmission [40], (ii) a contemporary geostatistical description of

P. falciparum malaria endemicity within these limits [41], and (iii)

updates of the modelled relationship between clinical incidence

and prevalence [42] have resulted in a substantially improved

evidence base from which to revisit estimates of the clinical burden

of P. falciparum, defined as the primary acute clinical event resulting

from malaria infection at all ages. Most significantly, a

geostatistical space–time joint simulation framework [43] is

combined with these improved cartographic and epidemiological

data sources to quantify uncertainty in the mapped outputs and to

propagate it appropriately into the derived burden estimates.

Using these joint simulation procedures we have built upon

previous approaches to produce the first continuous map of global

clinical P. falciparum incidence, and we use this to estimate the

global clinical burden of P. falciparum malaria in 2007. These

estimates are then compared with those available from surveil-

lance, and the opportunity for the further hybridization of these

techniques is discussed.

Methods

Analysis OutlineA schematic overview of the analysis procedures is provided in

Figure 1. In brief, of the 87 countries classified as endemic for P.

falciparum malaria [40], seven had sufficiently reliable health

information systems for case report data to be used directly to

enumerate clinical burden for 2007. We divided the population

at risk (PAR) in the remaining 80 countries into regions of

unstable and stable risk of transmission [40] (Figure 2). In

unstable regions, a uniform clinical incidence rate was adopted

of 0.1 case per 1,000 per annum (PA). This rate was multiplied

by a population surface [44] for 2007 (Figure 3) and aggregated

to obtain country and regional case estimates for these unstable

areas. Upper and lower bounds were defined using uniform rates

of zero and one case, respectively, per 1,000 PA. In stable

regions, we used a previously defined Bayesian geostatistical

model that took an assembly of space–time distributed P.

falciparum parasite rate (PfPR) surveys and generated realisations

of continuous age-standardized prevalence within the limits of

stable transmission [41]. We then used a Bayesian nonparamet-

ric model [42] of a collection of all-age active case detection

studies, to describe the uncertain relationship between the

clinical incidence rate and the underlying age-standardized

parasite prevalence. These two models were integrated in a

geostatistical space–time joint simulation framework to generate

joint realisations of clinical attack rate for every pixel as a

function of the predicted underlying prevalence [43] (Protocol

S1). These attack rates were then multiplied by the correspond-

ing pixel population totals to yield joint realisations of a clinical

burden surface (Figures 4 and 5). This joint simulation

framework supported the aggregation of per-pixel burden

estimates into defined spatial units, whilst preserving a space–

time uncertainty structure, allowing country and regional

estimates of burden to be made with appropriate credible

intervals (Table 1, Protocol S2). Each of these analytical

components are now discussed in more detail.

Defining Populations and Global RegionsThe Global Rural Urban Mapping Project (GRUMP) alpha

version [44] provides gridded population counts and population

density estimates for the years 1990, 1995, and 2000, adjusted to

the United Nations’ national population estimates. Population

counts for the year 2000 were projected to 2007 by applying

national, medium variant, intercensal growth rates [45] by country

using methods previously described [46] (Figure 3).

We have modified the World Health Organization (WHO)

regional country groupings, recognizing that these geopolitical

boundaries do not conform to the biogeographical determinants of

malaria risk and thus disease burden [41,47,48]. For the purposes

of disease risk estimation we have used three malaria regional

groupings: Africa+ (including Yemen and Saudi Arabia, which

share the same dominant Anopheles vectors as mainland Africa

[49]), the Americas, and the combined regions of Near East, Asia,

and the Pacific that we refer to as Central and South East (CSE)

Asia (Figure 2). To facilitate comparison with other estimates,

however, we have also shown the results aggregated by the

regional groupings of the WHO (Protocol S2).

Defining the Limits of Stable and Unstable P. falciparumTransmission

To define the global spatial limits of P. falciparum transmission,

we previously assembled confirmed P. falciparum clinical case

data for 41 P. falciparum malaria-endemic countries (PfMECs)

outside of Africa [40]. National case reported data were

expressed as P. falciparum annual parasite incidence (PfAPI)

derived from various combinations of active case detection

(fever surveys in communities where every person presenting

with a fever is tested for parasite infection) and passive case

detection (reports from febrile patients attending the local health

services) and usually expressed together as the number infected

per 1,000 PA [50–52]. These data were provided by malaria

coordinating officers in the WHO regional offices of the Eastern

Mediterranean (EMRO), Europe (EURO), South East Asia

(SEARO), and the Western Pacific (WPRO) at the highest

available administrative level unit between 2002 and 2007.

Among the countries in the American Regional Office (AMRO),

PfAPI data from national surveillance systems in Brazil,

Global Malaria Burden


Figure 1. Schematic diagram showing the procedure for burden estimation. Blue boxes describe input data, orange boxes models andexperimental procedures, dashed green rods intermediate output, and solid green rods the final output. The seven countries with reliable nationalreporting were Belize, Iran, Kyrgyzstan, Panama, Saudi Arabia, South Africa, and Tajikistan. The areas of unstable and stable transmission are definedas having less or more than one case per 10,000 PA, respectively [40,41].doi:10.1371/journal.pmed.1000290.g001

Figure 2. Global limits and endemicity of P. falciparum in 2007. The land area was defined as no risk (light grey), unstable risk (medium greyareas, where PfAPI ,0.1% PA), and stable risk (where PfAPI .0.1% PA) [40] with endemicity (PfPR in the 2- up to 10-year age group, PfPR2–10)displayed as a continuum of yellow to red between 0% and 100%. The dashed lines separate the Americas, Africa+, and the CSE Asia region,respectively, from left to right. The seven countries with thick blue borders have very low P. falciparum burden and reliable national healthinformation systems.doi:10.1371/journal.pmed.1000290.g002



Colombia, Peru, and Honduras were obtained directly from

personal communication with national malaria specialists.

The PfAPI data were mapped to first, second, or third

administrative level units and used to classify areas as no risk (zero

cases) and either unstable or stable risk if the number of confirmed

cases was lower or higher than 0.1 case per 1,000 PA, respectively

[40]. The unstable/stable classification was based on a review of

the statistical, logistical, and programmatic reasons underpinning

the PfAPI levels used to define phases and action points

during the Global Malaria Eradication Program [12,53–55]. In

addition, no transmission was assumed where medical intelli-

gence from international travel advisories or national malaria

control programmes stated no malaria risk or where the

temperature was too low for sporogony to complete within

the average lifespan of the local dominant vector species [49].

Measures of aridity were used to define areas in which

transmission is biologically plausible in isolated manmade

breeding sites, but overall transmission in surrounding areas is

limited by its effects on anopheline survival, and the clinical

incidence is likely to be less than 0.1 case per 1,000 PA. The

spatial extents of stable and unstable risk defined using these

inputs are shown (Figure 2).

Defining P. falciparum Clinical Incidence in Areas ofReliable Case Detection

Paradoxically, where the incidence of clinical malaria events are

rare, their rapid detection and notification becomes increasingly

important as part of national malaria control strategies, demanding

more sophisticated surveillance [51,55–57]. This is particularly true

for countries aiming to attain or maintain WHO accredited

elimination status [58–60]. Of the 87 PfMECs, we have identified

seven countries that are relatively wealthy and have specified a goal

Figure 4. Global clinical burden of P. falciparum in 2007. Bayesian geostatistical estimates (posterior means) of the number of all-age clinicalcases per 565 km pixel displayed on a logarithmic colour scale between 0 and 10,000 cases, within the stable limits of P. falciparum transmission.Dark and light grey areas are as described in Figure 2.doi:10.1371/journal.pmed.1000290.g004

Figure 3. Global human population density in 2007. Human population density [44] in persons per km2 is displayed on a logarithmic colourscale within the limits of P. falciparum transmission. No malaria risk is shown in light grey.doi:10.1371/journal.pmed.1000290.g003



of P. falciparum elimination where case-detection systems are an

integral part of the control strategies [58–60]: Panama, Belize,

Tajikistan, Kyrgyzstan, Iran, Saudi Arabia, and South Africa

(Figure 2). For these seven countries, we have used the national

reports for 2007 of all notified, locally acquired infections submitted

to regional WHO offices (see Acknowledgments) as the definitive

estimate of case burden. These countries are characterised by

having a small number of annual cases, with a large proportion of

the population living in areas of no risk or unstable transmission and

are therefore likely to represent a very small proportion of the global

P. falciparum malaria burden [40].

Defining Malaria Incidence in Areas of Unstable P.falciparum Malaria Transmission

We estimate that almost one billion people were living in areas

where P. falciparum transmission was unstable in 2007 [40]

(Figure 2). Defining annualized disease risk in these areas from

empirical data is difficult, as epidemiological investigations for

research or survey purposes are rare. Nevertheless, in computing

disease burdens it is important to impute some measure of

completeness of formal malaria reporting within these marginal,

unstable transmission areas. A number of malaria treatment-

seeking behaviour studies and qualitative examinations of routine

malaria reporting frequency suggest large inadequacies in a range

of national reporting systems from a variety of causes that can act

multiplicatively: Cambodia (actual number of cases 2.76 greater

than reported) [35], India (9–506) [28,61–65], Mozambique

(2.76) [32], Pakistan (5.96) [30], Peru (4.36) [34], Solomon

Islands (4.76) [38], Sri Lanka (1.96) [29], and Syria (4.56)

[31].

There are remarkably few specific investigations of the

completeness of malaria case notification systems in different

settings. Only four reports provide an estimate of the numbers of

cases likely to be missed by routine health system surveillance

compared to more aggressive, active case detection methods in the

same communities over the same time period. In the Yanomami

area of Brazil, approximately 1.25 more events were detected by

active detection than were reported to the routine health system

[57]. Across different years at different sites the ratio of active to

routine, passive detection varied from 4.5 to 42.1 in Vietnam [66],

with similar under-reporting rates documented in Cambodia [67].

A 5-fold difference in survey-to-passive rates of case detection has

been reported in Yunnan Province in China [68]. It is not possible

to provide an evidence-based under-reporting correction factor that

Figure 5. Uncertainty in the global clinical burden of P. falciparum in 2007. Bayesian geostatistical model-based prediction uncertainty(posterior standard deviations) on a logarithmic colour scale between 0 and 20,000 cases, within the stable limits of P. falciparum transmission. Nomodel-based uncertainty metrics were produced for areas of unstable transmission. Dark and light grey areas are as described in Figure 2.doi:10.1371/journal.pmed.1000290.g005

Table 1. Numbers of Plasmodium falciparum clinical attacks by region globally in 2007.

Category Americas (16 countries) Africa+ (47 countries) CSE Asia (19 countries) Total

Reliable reporting (casesa) 32 (Panama, Belize) 2,717b (Saudi Arabia, SouthAfrica)

618 (Kyrgyzstan, Tajikistan, Iran) 3,367

Unstable riskc (casesa) 5,455 (0–54,550) 1,892 (0–18,920) 98,049 (0–980,490) 105,395 (0–1,053,950)

Stable riskc (millions of casesa) 3.04 (1.17–6.70) 270.88 (241.13–300.56) 176.90 (89.21–269.58) 450.83 (348.76–552.22)

Total (millions of casesa) 3.05 (1.17–6.76) 270.89 (241.13–300.58) 177.00 (89.21–270.56) 450.93 (348.76–553.27)

The regional groupings are illustrated in Figure 1.aCase numbers from countries with reliable reporting and areas of unstable risk are presented directly whilst those from areas of stable risk are presented in millions ofcases, rounded to the nearest 10,000, reflecting the larger numbers and lower precision associated with these model-based estimates.

bPresumed to be all P. falciparum, although autochthonous case reports did not specify.cExcluding countries with reliable case data.doi:10.1371/journal.pmed.1000290.t001



is specific for every national malaria information system. We have

therefore elected to use a single worst-case rate of 10-fold under-

reporting across all countries. We hence assume for all unstable

areas a uniform incidence of 0.1 case per 1,000 PA, with a lower

confidence bound of zero and an upper confidence bound assuming

a 10-fold under-reporting rate; equating to one case per 1,000 PA.

Defining Malaria Incidence in Stable Endemic AreasWe estimated that in 2007, approximately 1.4 billion people

lived in areas of stable P. falciparum transmission [40] (Figure 2). In

these areas, we considered that case-reporting through routine

health information systems was too unreliable for the calculation of

incidence due to inadequate reporting coverage (see above),

widespread self-medication [27], and poor diagnosis [21,25].

Instead, we developed a model-based cartographic method for

deriving estimates in the areas of stable transmission in which

clinical incidence was modelled as a function of the underlying

endemicity (parasite prevalence). This procedure required: (i) a

spatially continuous model for endemicity; (ii) a further model to

predict incidence as a function of endemicity; (iii) reliable data on

2007 population distribution; and (iv) a technique for combining

these components so that the uncertainty inherent in the

component models was propagated into the resulting burden

estimates. These components are now outlined in turn, with

additional statistical details provided in Protocol S1.

To estimate stable transmission intensity, a Bayesian space-time

geostatistical modelling framework was developed to interpolate

empirical estimates of age-corrected parasite prevalence derived

from 7,953 community surveys undertaken between 1985 and

2008 across 83 malaria-endemic countries. This model has been

described in detail elsewhere [41] and its output allows for a

continuous, urban-adjusted, contemporary estimate of parasite

prevalence in children aged from 2 up to 10 years (PfPR2–10) at a

pixel spatial resolution of 565 km for the year 2007 (Figure 2).

To estimate clinical incidence, formal literature searches were

conducted for P. falciparum malaria incidence surveys undertaken

prospectively through active case detection at least every 14 days

[42]. The incidence surveys were time–space matched with

estimates of parasite prevalence derived from the geostatistical

model described above [41]. Potential relationships between all-

age clinical incidence and age-standardized parasite prevalence

were then specified in a nonparametric Gaussian process model

with minimal, biologically informed, prior constraints. A temporal

volatility model was incorporated to describe the variance in the

observed data and Bayesian inference was used to choose between

the candidate models [42]. Separate relationships were preferred

for each of the three regions defined globally (Figure 2) to

accommodate regional-specific differences in the dominant vector

species [47,49,69], the impact of drug resistance on recrudescent

clinical attacks [70], the possible modification of P. falciparum

clinical outcomes in areas of P. vivax co-infection [71,72], and the

genetic contribution to disease risk of inherited haemoglobin

disorders [73]. Due to the sparse data in the Americas, however,

this region was combined with CSE Asia. In the Africa+ region

and the combined Americas and CSE Asia region, clinical

incidence increased slowly and smoothly as a function of infection

prevalence (Figures 6, 7, 8, and 9). In the Africa+ region, when

infection prevalence exceeded 40%, clinical incidence reached a

maximum of 500 cases per 1,000 PA (Figure 6). In the combined

Americas and CSE Asia regions this maximum was reached at 250

cases per 1,000 PA (Figure 7).

Both the geostatistical endemicity and the endemicity–

incidence models were specified in a fully Bayesian framework.

The output of the former was a large set of realisations

(n = 250,000): possible maps that, together, represented the

modelled uncertainty in endemicity at each location. Similarly,

the output of the endemicity–incidence model was a large set

(n = 250,000) of possible forms of the endemicity-incidence curve

that encompassed the modelled uncertainty in this relationship

(Figures 6, 7, 8, and 9). To combine the uncertainty from both

models, each realisation of the uncertainty map was used as input

into a realisation of the endemicity–incidence model to obtain a

realisation of a 565 km resolution incidence map. This was

downscaled to 161 km resolution and multiplied with the 2007

population surface to obtain, for every grid square, a realisation

of the number of clinical cases in 2007. By repeating this

Figure 6. The posterior distribution of the prevalence-inci-dence relationship (h �pp,Tð Þ, see Methods) in the Africa+ region.The relationship is plotted between malaria endemicity (PfPR in the 2-up to 10-year age group, PfPR2–10) and all-age incidence (clinical casesper thousand of the population PA) [42]. Please see reference [42] for afull description of the data, methods, and techniques used to define thisrelationship. The light grey, medium grey and dark grey regions definethe 95%, 50%, and 25% credible intervals, respectively. The solid blackline is the median and the data are shown as red dots.doi:10.1371/journal.pmed.1000290.g006

Figure 7. The posterior distribution of the prevalence-inci-dence relationship (h �pp,Tð Þ, see Methods) in the combined CSEAsia region and the Americas. The techniques and colours used areidentical to Figure 6.doi:10.1371/journal.pmed.1000290.g007



procedure for every model realisation, a set of 250,000 burden

values was generated for every grid square, approximating a

complete posterior distribution for the estimates. Because each

realisation of the endemicity map was jointly simulated, rather

than calculated on a pixel-by-pixel basis, each realisation of

burden could be aggregated spatially or temporally, whilst

maintaining the correct variance structure. This allowed burden

realisations at each pixel to be combined spatially to generate

estimates of national and regional burdens with appropriate

credible intervals. Joint simulation at this scale is enormously

computationally intensive and a bespoke algorithm was devel-

oped to implement this stage of the analysis. The algorithm is

presented elsewhere [43] and the statistical details are summa-

rised in Protocol S1.

Results

The combined clinical burden of the seven nations with

comprehensive reporting was 3,367 cases in 2007 (Table 1,

Protocol S2). Multiplying the population surface (Figure 3) by the

assumed incidence rate in unstable areas (see Methods) produced

an estimate of 105,395 clinical cases of P. falciparum malaria in

areas of unstable transmission (Table 1, Protocol S2), with a

plausible range between zero and 1,053,950. The modelling

procedures in the stable areas generated an estimate of 451 million

cases (lower 95% credible interval 349 million and upper 95%

credible interval 552 million) of P. falciparum malaria in areas of

stable transmission in 2007, of which 271 (241–301) million were

estimated to have occurred in the Africa+ region, 177 (89–270)

million in the CSE Asia region and 3 (1–7) million in the Americas

(Table 1).

Combining our estimates from the seven countries with

comprehensive case reporting with those from areas of unstable

and stable transmission in the remaining 80 PfMECs, we estimate

that in 2007 there were 451 (349–553) million clinical cases of P.

falciparum malaria. A continuous map of these incidence predic-

tions is provided (Figure 4), with an additional map of the pixel-

specific uncertainty (Figure 5). In addition to the regional

summaries presented (Table 1), estimates of clinical burden are

summarized for each country and for each of the WHO global

regions (Figure 10 and Protocol S2). It is notable that more than

half (51%) of the world’s estimated P. falciparum clinical cases

derive from just four countries: India, Nigeria, DRC, and

Myanmar (Burma) (Figure 4 and Protocol S2) and that, in

addition, these nations contribute 48% of the uncertainty (Figure 5)

in the global incidence estimates.

Regional summary estimates of P. falciparum malaria cases in

unstable and stable transmission areas are summarized in Table 1

and are also shown for the WHO regions in Figure 10. It is clear

that African populations suffered the largest proportion (60%) of

the 451 million clinical cases of P. falciparum estimated globally in

2007 (Figure 10, Table 1 and Protocol S2). The highest-burden

countries in Africa are Nigeria and DRC, both countries with

extensive regions of high endemicity (Figure 2) and large

populations (Figure 3). These two countries account for 23% of

the world’s P. falciparum disease burden (Protocol S2). Less than 1%

of the global P. falciparum burden occurred in the Americas, where

transmission intensity is almost universally low or unstable

(Figure 2). We estimate that the remaining 39% of global burden

in 2007 occurred in the CSE Asia region (Table 1). In this region,

the immense population living at risk of P. falciparum malaria

means that, despite a low prevalence [41] (Figure 2) and the lower

endemicity–incidence relationship [42] (Figure 7), cases in CSE

Asia add substantially to the global disease burden (Table 1). At a

country level, India and Myanmar contribute 22.6% and 5.8%,

respectively, of the total number of clinical cases due to P.

falciparum worldwide (Protocol S2).

Discussion

We have used a combination of methods, including a joint

simulation of incidence in areas of stable transmission, to estimate

451 (349–552) million clinical cases of P. falciparum malaria in

2007: 3 (1–7) million in the Americas, 271 (241–301) in the

Africa+ region, and 177 (89–270) in the CSE Asia region.

Figure 8. The predictive distribution of the incidence thatwould actually be observed by weekly surveillance over a two-year period in the Africa+ region. Please see reference [42] for a fulldescription of the data, methods, and techniques used to define thisrelationship. The light grey, medium grey, and dark grey regions definethe 95%, 50%, and 25% credible intervals, respectively. The solid blackline is the median and the data are shown as red dots. Note that thedata points were collected using different surveillance intervals overdifferent time periods, and therefore should not be expected to followthe distribution predicted by the model exactly. The observedincidences are included in the figure as a visual aid only.doi:10.1371/journal.pmed.1000290.g008

Figure 9. The predictive distribution of the incidence thatwould actually be observed by weekly surveillance over a two-year period in the combined CSE Asia region and the Americas.The techniques and colours used are identical to Figure 8.doi:10.1371/journal.pmed.1000290.g009



Morbidity in Areas of Unstable TransmissionWe have accepted as accurate the surveillance reports of seven

relatively high income and low burden PfMECs, all nations with

credible plans for malaria elimination [59,60,74–76]. We have

further attempted to describe clinical disease incidence in areas of

the world that we classify as unstable risk [40], which were home

to almost a billion people in 2007. We know relatively little about

the epidemiology of P. falciparum in the 40% of the global PAR of

P. falciparum malaria living in unstable transmission areas. These

areas are notoriously difficult to define in terms of potential

disease outcomes; they may go several years without a single

autochthonous case, transmission is extremely focal and,

importantly, investigation of the clinical epidemiology is prohib-

itively expensive because of the rarity of the disease [77]. We

have, therefore, defaulted to national reporting systems as an

entry point to the definition of risk and have used surveys of

under-reporting rates to define plausible ranges of the disease

burden in these marginal transmission zones. We estimate that

there were 105,395 (0–1,053,950) cases of P. falciparum in unstable

transmission areas in 2007. Despite being relatively crudely

defined, these sums represent only 0.02% of the global clinical P.

falciparum burden. Therefore, while these cases are of significant

concern to those nations with large populations at unstable risk

and to those considering elimination [59,60,74–76], they make a

very small contribution to the estimation of the global P.

falciparum burden.

Morbidity in Stable AreasWe have improved upon a P. falciparum disease burden

estimation rubric that has been used several times previously for

Africa [1,3,4,6,7] and once before globally [5]. This method

requires an understanding of the basic clinical epidemiology of P.

falciparum malaria, its relationship to transmission intensity and the

use of empirical, longitudinal observations in populations exposed

to different conditions of transmission. However, these empirical

studies of clinical incidence are not without their own caveats [42].

Longitudinal surveillance over a complete annual malaria

transmission cycle within the same cohort is likely to underesti-

mate the ‘‘natural’’ risk of disease given the ethical need to treat

effectively all detected infections or clinical events. These studies

are also conducted throughout a range of region-specific co-species

infection [78], HIV/AIDS prevalence [79], and drug resistance

[80] conditions. The number of studies meeting our inclusion

criteria remains low, so these covariate determinants of clinical risk

cannot be adequately modelled or controlled for in this series [42].

We have considered all infections that are associated with a

reported or measured febrile event as clinical malaria. This seems

appropriate under conditions of low transmission intensity, but as

transmission intensity increases, the proportion of fevers that can

be causally linked to malaria infection declines [26,81]. Conse-

quently, our estimates of clinical attack rates at the highest levels of

transmission are likely to be overestimates of true P. falciparum

clinical incidence. Locally derived age- and transmission-depen-

dent aetiological fraction estimates were not available for the

majority of studies in order to allow the application of meaningful

corrections. Conversely, the use of fever and any level of

peripheral infection to define a malaria case corresponds closely

to the criteria recommended for case treatment across the world

[82,83] and thus has congruence with disease burdens that should

be managed with appropriate medicines. Finally, we have not

considered the impact of scaled or partial coverage of interventions

aimed at preventing infection, because we feel this is reflected in

the parasite prevalence surface [41]. The one exception is the use

of failing monotherapy because recrudescent cases will not be

reflected in our endemicity–incidence relationship based on active

case detection with effective treatment and thus, where this poses a

significant threat, our estimates will be even greater underesti-

mates. Despite the caveats, we believe that this approach to P.

falciparum disease burden estimation provides an alternative and, in

nations with inadequate surveillance, the only existing approach to

estimating the true global risk of malaria.

Robust Estimates of UncertaintyWe have used joint simulations from an established Bayesian

geostatistical model for P. falciparum parasite prevalence in the 2-

up to 10-year age group (PfPR2–10) (Figure 2), integrated with a

second Bayesian model for the endemicity-incidence relationship

(Figures 6 and 7), to generate spatially distributed estimates of the

clinical burden of P. falciparum malaria worldwide with associated

uncertainty. This reflects the uncertainty in measures of risk that

results in a range of possible estimates globally from 349 to 553

million cases in 2007; similar to the range size in other malaria

burden estimations [1,3,5,7,84]. This elaborate modelling frame-

work has allowed the incorporation of uncertainty in our

knowledge of the intensity of transmission at any given location

with uncertainty in our knowledge of how this intensity influences

the rate of clinical episodes at that location, allowing the net

uncertainty to be propagated into final estimates of clinical

burden. Crucially, the joint simulation framework allows modelled

uncertainty to be aggregated across regions to provide our final

credible intervals for country and region-specific burden estimates,

a procedure that is not possible using the per-pixel prediction

approaches currently pervasive in disease mapping.

Figure 10. Pie chart of P. falciparum clinical cases in 2007. The pie chart shows the fraction of the 451 million cases of total clinical burden ineach of the World Health Organization regions (Protocol S2). In the pie the regions are ordered counterclockwise starting at the top, from highest tolowest burden. The plotted area representing the EURO region is too thin to be visible. The thumbnail map shows the country composition of theWHO regions for all 87 P. falciparum endemic countries.doi:10.1371/journal.pmed.1000290.g010



The WHO has recently used surveillance-based techniques to

estimate the combined burden of P. falciparum and P. vivax to be

247 million cases in 2006 (189–287) [8]. The WHO placed greater

reliance on data reported routinely through national health

management information systems (HMIS), which were subjected

to a range of evidence-based adjustments for nonattendance,

reporting rates, and diagnostic practices. These HMIS data were

used for national estimates in 77 of 107 countries considered

worldwide (Protocol S2). The fidelity of these estimates and their

sensitivity to assumptions underlying the suite of adjustment

factors was dependent on the quality and completeness of the

HMIS data from each country. In the 30 countries with the least

reliable national data, a predecessor of the prevalence-based

modelling protocol presented in this study was used [8,85]. The

results are shown for individual countries in Protocol S2. These

estimates were revised in 2009 but data have not been made

available for all countries [9].

Uncertainty in IndiaIndia is a country of considerable diversity in its current and

historic malaria ecology, a country which suffered in excess of a

million deaths PA during the colonial era [86]. Since its

independence in 1947, India has achieved remarkable malaria

control gains, reducing morbidity to 100,000 cases and mortality

to zero in 1965 [87] at the peak of the Global Malaria Eradication

Programme [53]. Since this time malaria resurgence has been

widely reported in the country [87–89]. The contemporary

burden is unknown [90–97] and is probably exacerbated by the

unique problem of urban malaria, maintained by Anopheles stephensi

[49,88,98].

India remains a massive source of uncertainty in our

cartography-based estimates (Results and Protocol S2), contribut-

ing over three-quarters (76%) of the uncertainty range in the

global incidence estimates. It is therefore important to explore

ancillary evidence for the plausibility of these cartographic

estimates of 102 (31–187) million compared to the much smaller

estimate derived from surveillance-based techniques: 10.65 (9.00–

12.41) million [8].

A wide range of factors can reduce the accuracy of surveillance

data. Low rates of care-seeking for malaria in the formal health

sector, unreliable diagnoses, poor record keeping, and inefficient

data transfer and collation systems can all combine to make the

number of cases formally reported a small fraction of the true

number of cases in a population. To mitigate these substantial

sources of bias in raw surveillance data, the approach taken by

WHO is to modify the raw data using a number of adjustment

parameters, which can include the proportion of people with fever

seeking formal-sector care, the reporting rate by facilities, and the

likely positivity rates amongst non-attending and non-slide–

confirmed cases of fever [8,85]. Such adjustments are essential,

but the validity of the final estimate is entirely dependent on the

values used for each parameter, which are drawn from a mixture

of health-system reported figures, secondary data of varying

fidelity, and ad-hoc decision rules. A key weakness of this approach

is that, in many cases, the true uncertainty around key parameter

values is not captured adequately.

In the case of India, raw surveillance data for 2006 reported 1.8

million malaria cases. Adjustments were made for care-seeking

behaviour and reporting rate by health facilities, which combined

to increase the estimate by a factor of 5.0–6.9, to the final figure of

10.65 (9.00–12.41) million [8], with the confidence range

primarily reflecting differing assumptions for positivity rate

amongst nonpresenting fevers. Assessing the validity of either the

individual adjustment parameters or the final estimate is difficult

since, by definition, gold-standard values for comparison do not

exist. However, numerous studies in India have compared case

numbers detected via routine surveillance with parallel commu-

nity-based longitudinal surveys and found disparities much larger

than the factor of approximately six used by the WHO. For

example, malaria incidence in the Kichha Primary Health Centre

(PHC) and Kharkhoda PHC were 23.5 and 38.9 times under-

reported, respectively [61]. Large discrepancies were also reported

in Gadarpur PHC (53.56) [62], Nichlaul PHC (20.36) [64] and

Ahmedabad City (96) [65]. For India, the WHO estimate makes

no allowance for misdiagnosis within the formal health sector,

although studies have shown that this can be substantial. In the

PHCs of ten districts in Uttar Pradesh, 75% of slide-confirmed

infections were missed when the slides were checked by a reference

centre [28], and an estimated 58% were missed in Bisra PHC

when fortnightly rather than weekly surveillance was used [63].

In completely independent work, the final estimate for malaria

mortality in India in 2006, taken from the ‘‘million deaths’’ verbal

autopsy study was approximately 200,000 deaths (Dhingra N, et

al., unpublished data). Assuming a conservative case fatality rate of

only one per 1,000 [99,100], this would lead to a morbidity

estimate much closer to those retrieved using cartographic

techniques—somewhere in the region of 200 million cases. Similar

arguments of plausible morbidity totals can be made using other

recent mortality estimates of 50,000 deaths in 1998 in 15 of 38

States and Union Territories [90,93]. In sum, we find that

cartography-based estimates are supported by, and resonate most

closely with, the findings in the recent literature [90–96], although

it should be acknowledged that there is likely to be a publication

bias in reports of problems over progress.

There is no perfect post-hoc correction to compensate for poor

malaria surveillance. Both methods using routine HMIS adjusted

for nonattendance, poor reporting, and inadequate diagnostics,

and those presented here, have limitations with respect to coverage

and quality of the input data for each model, and with respect to

underlying modelling assumptions. Both approaches to burden

estimation result in wide margins of confidence and the inevitable

plea from any such analysis is for accurate national reporting

systems or more empirical epidemiological data. It can be seen

clearly from these analyses that improvements in basic malario-

metric information in only four countries would radically reduce

uncertainty in the global estimates of the malaria burden.

Additionally, the approach presented does provide a standardized

method across all malaria-endemic countries, using a set of

transparent epidemiological rules allowing countries to be

compared without concerns about differences in national health

information quality or coverage.

A Hybrid Approach?To allay some of the concerns about the use of cartographic

techniques in low-endemicity settings [101], we have also

investigated the possibility of combining the two burden estimation

processes for the 87 PfMECs.

Seven countries have ‘‘gold-standard’’ reporting systems

requiring no adjustment by either technique. These are in the

African Regional Office (AFRO): South Africa; in AMRO: Belize

and Panama; EMRO: Iran and Saudi Arabia; and EURO:

Kyrgyzstan and Tajikistan (7/87). In many PfMECs in the Africa+region, an outdated cartographic technique was used by WHO

[8]. Since the new methods outlined here are an unambiguous

improvement, these were adopted for the following PfMECs: in

AFRO: Angola, Burkina Faso, Cameroon, Central African

Republic, Chad, Congo, Cote d’Ivoire, DRC, Equatorial Guinea,

The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Malawi,



Mali, Mauritania, Mozambique, Niger, Nigeria, Sierra Leone,

Togo, Uganda, and Zimbabwe; and in EMRO: Yemen (25/87).

In addition, Mayotte in AFRO and French Guiana in AMRO

have no WHO estimates, so we default to the cartographic

approach (2/87). Conversely there are two small island nations in

AFRO (Cape Verde and the Comoros) for which we had no

contemporary PfPR data and the spatial resolution of mapping

was not ideal, so the WHO estimates were used (2/87).

We then calculated, for all countries, the ratio of the width of

the 95% credible interval to the point estimate obtained using the

cartographic method and ranked this relative uncertainty metric

by nation (Protocol S2). For those countries where this

cartography-based uncertainty ranked in the bottom half (i.e.,

the least uncertain, corresponding to a ratio of ,40), we adopted

our cartographic-based estimates. They were in AFRO: Benin,

Burundi, Ethiopia, Gabon, Kenya, Madagascar, Rwanda, Sene-

gal, United Republic of Tanzania, and Zambia; in EMRO:

Somalia and Sudan; in SEARO: India, Indonesia, and Myanmar;

and in WPRO: Papua New Guinea (16/87). Conversely, in

countries where cartography-based uncertainty was ranked in the

top half (ratio $40) we defaulted to the WHO estimate. They were

in AFRO: Botswana, Eritrea, Namibia, Sao Tome and Prıncipe,

and Swaziland; in AMRO: Bolivia, Brazil, Colombia, Dominican

Republic, Ecuador, Guatemala, Guyana, Haiti, Honduras,

Nicaragua, Peru, Suriname, and Venezuela; in EMRO: Afghani-

stan, Djibouti, and Pakistan; in SEARO: Bangladesh, Bhutan,

Nepal, Sri Lanka, Thailand, and Timor-Leste; and in WPRO:

Cambodia, China, Lao People’s Democratic Republic, Malaysia,

Philippines, Solomon islands, Vanuatu, and Viet Nam (35/87).

This hybrid approach resulted in seven countries using gold

standard national reports, 43 nations using cartographic tech-

niques and 37 using the surveillance-based methods of WHO. The

percentage of the global burden estimated by each technique was

0.001%, 97.722%, and 2.277%, respectively. Using a hybrid

approach therefore makes very little difference to the global

clinical burden estimate for 2007, although it has a significant

impact on the absolute number of cases estimated for each country

(Protocol S2).

Interpreting EstimatesThese estimates improve upon previous efforts, which used

epidemiological approaches to estimate the global burden of P.

falciparum clinical attacks in 2002 (515 million, interquartile range

300–660 million) [5], and more recent efforts to estimate

paediatric clinical events due to high parasite densities of P.

falciparum in Africa in 2000 (116 million, uncertainty interval 91–

258 million) [7]. The differences between these results and

previous efforts are not primarily due to differences in the base

year of analysis or definitions of a clinical attack, but stem largely

from differences in estimation of the endemicity-structured PARs.

In our previous global estimates [5], we adapted a historical,

categorical description of malaria endemicity, whilst in Africa we

[1,3,4] and others [6,7] have previously used a climate suitability

model of the likelihood of stable transmission as an index of

differences in transmission intensity [102,103]. The single largest

difference between previous work and the present iteration of P.

falciparum disease burden estimation is that neither previous

approach was based upon an empirically defined risk map of

malaria transmission [41]. Comparing estimates derived using

these different techniques, over various time periods, is not a sound

basis for investigating trends and should be avoided.

It is clear that investing in radically improved surveillance and/

or nationally representative malariometric surveys would substan-

tially increase the fidelity of national and, by extension, global

burden estimates. Because there are regional differences in the

uncertain relationship between transmission intensity and disease

outcome [42], more information derived from active case

detection studies would improve the precision in our estimates of

disease incidence within these transmission ranges. This informa-

tion, while welcome, is likely to make only small differences to the

computed risk in most scenarios of malaria transmission defined

here. As a consequence, we believe that until there is a universally

reliable reporting system for malaria cases worldwide to support

comprehensive surveillance-based estimates, a concerted effort to

map the changing spatial extents and intensity of transmission will

remain a valuable contribution to the future estimations of a

changing disease burden worldwide. In the short term, measuring

how the ‘‘denominator’’ changes with time is clearly easier and

cheaper than improving the global state of health information

systems.

Future DirectionsMany improvements will be possible with further work. We

have not stratified incidence by age nor considered any of the

consequential morbid events, sequelae, or mortality. Systematic

biases in the identification of the extent of stable and unstable

transmission would clearly impact estimates, and developing the

datasets and techniques to address this problem is an important

avenue for future work. Nor have we modelled uncertainty in

HMIS reporting in unstable and low-stable transmission zones,

and this might be possible with a methodological hybrid

combining higher spatial resolution HMIS facility data with

geostatistical techniques [37]. Moreover, we have not been able to

consider some sources of uncertainty in the current framework; for

example, those concerning the enumeration of the underlying

population, based on collated census data; urban extent maps; and

UN population projections. Finally, we have not considered the

morbid burden posed by P. vivax. There are important differences

in the biology of P. vivax [104] which make its control [105], and

thus cartography-based burden estimation, problematic: its

tendency to cause relapses [106], the routine reliability of parasite

diagnosis when coincidentally prevalent with P. falciparum

[107,108] and the less well-defined relationship between trans-

mission intensity and disease outcome. These all make an

informed cartography of P. vivax distribution and estimations of

disease burden considerably more complex than for P. falciparum.

We do not underestimate the likely disease burden of P. vivax

malaria [109–112], but new, innovative approaches based on an

understanding of the clinical epidemiology and better cartography

are required to improve upon current efforts to define the burden

due to P. vivax.

It is worth reiterating that if the international community wishes

to demonstrate progress in malaria control, then the quantity and

timeliness of prevalence information and parasite-specific surveil-

lance records must dramatically improve. This is true for all

countries but is particularly important in India, Nigeria, DRC,

and Myanmar because of the large populations at risk and the

paucity of existing malariometric information. These improve-

ments in information collection and provision are as important

across space (to be geographically representative of all transmission

settings and intervention scenarios) as they are through time, so

that impact can be evaluated in a timely manner. Conceptually,

we also envisage that significant progress will be made in

improving the accuracy of these estimates by hybridising

cartographic and surveillance-based approaches. This would be

best achieved by combining geopositioned HMIS facility data with

geostatistical model outputs [37], so that the relative uncertainty of

each can be compared and complementary information from both



sources combined in a single coherent spatial framework. Globally,

this is likely to be of particular utility in those areas of low and

unstable transmission where surveillance capabilities are often

more robust and correspondingly where prevalence data are often

rare as the number of people needed to be sampled to find

infections is prohibitive [12].

The malaria clinical burden estimates presented in this paper

are driven by the underlying model of global prevalence [41]. This

global malaria map is, to our knowledge, the first evidence-based

attempt to define populations at risk of different levels of parasite

transmission. It is needed in order to define the ranges of disease

outcomes at a global scale and can serve as the benchmark for

malaria disease burden estimations. The map will inevitably

change with time as new information on the spatial extents of

transmission and new PfPR2–10 data become increasingly available

with the scale-up of interventions. The time–space functionality of

the geostatistical model will increasingly capture the effects of

scaled intervention efforts to reduce transmission, causing the size

of the PfPR used to compute disease burden to change. Revising

the limits and endemicity maps from this baseline and propagating

these changes through to revised enumerations of clinical burden

thus represents a useful complementary technique to assessing the

impact of financing [113] on our progress towards international

development targets for reducing malaria burden [59,114].

Supporting Information

Protocol S1 Supplemental methods.

Found at: doi:10.1371/journal.pmed.1000290.s001 (1.39 MB

DOC)

Protocol S2 A comparison of cartographic and surveillance-

based estimates of national clinical incidence.

Found at: doi:10.1371/journal.pmed.1000290.s002 (0.34 MB

DOC)

Acknowledgments

We thank Kevin Baird, Simon Brooker, Archie Clements, and Dave Smith

for comments on the manuscript, and Anja Bibby for proofreading. The

data used in this paper were critically dependent on the contributions 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.html). Mikhael Ejov (Kyrgyzstan,

Tajikistan), Rainer Escalada (Panama, Belize), Rajendra Maharaj (South

Africa), and Rakesh Rastogi and Ghaseem Zamani (Saudi Arabia, Iran) are

thanked for help with regional case data and country-specific data where

specified. The authors acknowledge the support of the Kenyan Medical

Research Institute (KEMRI). This paper is published with the permission

of the director of KEMRI.

Author Contributions

ICMJE criteria for authorship read and met: SIH EAO PWG APP AJT

CAG RWS. Agree with the manuscript’s results and conclusions: SIH

EAO PWG APP AJT CAG RWS. Designed the experiments/the study:

SIH RWS. Analyzed the data: SIH PWG APP AJT RWS. Collected data/

did experiments for the study: SIH EAO PWG AJT CAG RWS. Wrote the

first draft of the paper: SIH RWS. Contributed to the writing of the paper:

EAO PWG APP AJT CAG RWS.

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Editors’ Summary

Background. Malaria is a major global public-healthproblem. Nearly half the world’s population is at risk ofmalaria, and Plasmodium falciparum malaria—the deadliestform of the disease—causes about one million deaths eachyear. Malaria is a parasitic disease that is transmitted topeople through the bite of an infected mosquito. Theseinsects inject a parasitic form known as sporozoites intopeople, where they replicate briefly inside liver cells. The livercells then release merozoites (another parasitic form), whichinvade red blood cells. Here, the merozoites replicate rapidlybefore bursting out and infecting more red blood cells. Thisincrease in the parasitic burden causes malaria’scharacteristic symptoms—debilitating and recurring feversand chills. Infected red blood cells also release gametocytes,which infect mosquitoes when they take a blood meal. In themosquito, the gametocytes multiply and develop intosporozoites, thus completing the parasite’s life cycle.Malaria can be prevented by controlling the mosquitoesthat spread the parasite and by avoiding mosquito bites.Effective treatment with antimalarial drugs also helps toreduce malaria transmission.

Why Was This Study Done? In 1998, the World HealthOrganization (WHO) and several other international agencieslaunched Roll Back Malaria, a global partnership that aims toprovide a coordinated, global approach to fighting malaria.For this or any other malaria control initiative to be effective,however, an accurate picture of the global clinical burden ofmalaria (how many people become ill because of malariaand where they live) is needed so that resources can beconcentrated where they will have the most impact.Estimates of the global burden of many infectious diseasesare obtained using data collected by national surveillancesystems. Unfortunately, this approach does not work verywell for malaria because in places where malaria is endemic(always present), diagnosis is often inaccurate and nationalreporting is incomplete. In this study, therefore, theresearchers use an alternative, ‘‘cartographic’’ method forestimating the global clinical burden of P. falciparum malaria.

What Did the Researchers Do and Find? The researchersidentified seven P. falciparum malaria-endemic countries thathad sufficiently reliable health information systems todetermine the national clinical malaria burden in 2007directly. They divided the other 80 malaria endemic countriesinto countries with a low risk of transmission (unstabletransmission) and countries with a moderate or high risk oftransmission (stable transmission). In countries with unstabletransmission, the researchers assumed a uniform annualclinical incidence rate of 0.1 cases per 1,000 people andmultiplied this by population sizes to get disease burdenestimates. In countries with stable transmission, they used amodeled relationship between clinical incidence (number ofnew cases in a population per year) and prevalence (theproportion of a population infected with malaria parasites)and a global malaria endemicity map (a map that indicates

the risk of malaria infection in different countries) to estimatemalaria incidences. Finally, they used a technique called‘‘joint simulation’’ to quantify the uncertainty in theseestimates. Together, these disease burden estimates gavean estimated global burden of 451 million clinical cases of P.falciparum in 2007. Most of these cases occurred in areas ofstable transmission and more than half occurred in India,Nigeria, the Democratic Republic of the Congo, andMyanmar. Importantly, these four nations alonecontributed nearly half of the uncertainty in the globalincidence estimates.

What Do These Findings Mean? These findings areextremely valuable because they provide a global map ofmalaria cases that should facilitate the implementation andevaluation of malaria control programs. However, theestimate of the global clinical burden of P. falciparummalaria reported here is higher than the WHO estimate of247 million cases each year that was obtained usingsurveillance-based methods. The discrepancy between theestimates obtained using the cartographic and thesurveillance-based approach is particularly marked forIndia. The researchers discuss possible reasons for thesediscrepancies and suggest improvements that could bemade to both methods to increase the validity and precisionof estimates. Finally, they note that improvements in thenational prevalence surveys in India, Nigeria, the DemocraticRepublic of the Congo, and Myanmar would greatly reducethe uncertainty associated with their estimate of the globalclinical burden of malaria, an observation that shouldencourage efforts to improve malaria surveillance in thesecountries.

Additional Information. Please access these Web sites viathe online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000261.

N A PLoS Medicine Health in Action article by Hay andcolleagues, a Research Article by Guerra and colleagues,and a Research Article by Hay and colleagues providefurther details about the global mapping of malaria risk

N Additional national and regional level maps and moreinformation on the global mapping of malaria are availableat the Malaria Atlas Project

N Information is available from the World Health Organiza-tion on malaria (in several languages)

N The US Centers for Disease Control and Prevention provideinformation on malaria (in English and Spanish)

N Information is available from the Roll Back MalariaPartnership on its approach to the global control ofmalaria (in English and French)

N MedlinePlus provides links to additional information onmalaria (in English and Spanish)



1

Protocol S1: Supplemental Methods

S1.1. Statistical Model for P. falciparum Prevalence Full details of the geostatistical model for P. falciparum prevalence are provided in the

supplementary information: “Protocol S3” of Hay et al. (2009) [1]. Briefly, the th observation

was modelled as binomially distributed, conditional on the sample size , the age limits

and a spatiotemporal random field evaluated at the location and time of the

survey. The “typical” prevalence was a composition of a link function, in this case the

standard inverse-logit, and a Gaussian process denoted . The random function mapped

and to community prevalence within the age limits of survey . The distributional

parameters of the unknown function were a mean function and a covariance function

, where denotes scalar parameters, including covariate coefficients [2,3]. The model

was completed by priors for . In probability notation:

(SA.1)

For clarity, this section will refer to this schematic representation of the model.

A major reason for the success of Gaussian processes in geostatistical modelling is the

fact that they are trivial to marginalize. The vector obtained by evaluating at all the

survey locations and times has a multivariate normal distribution:

(SA.2)

The vector is constructed in the same way as , and the matrix is

defined as:

(SA.3)

Conditional on , is independent of evaluated at all other locations, so this

marginalization can be used to reduce to a simple multivariate normal variable at the

model-fitting stage.

2

Sampling the Posterior Predictive Distribution

The model was fitted using the Markov chain Monte Carlo (MCMC) algorithm [4], which

produces a sequence of samples from the joint posterior of and . Conditional on one

of these samples, the value of at unobserved location and time can be sampled from

its posterior predictive distribution:

(SA.4)

where the posterior predictive parameters are given by the standard conditioning formulas for

multivariate normal variables [5]:

(SA.5)

These formulas apply regardless of whether denotes multiple prediction locations or a

single point. Samples from the predictive distribution for many values of

and from the MCMC trace can be regarded as samples from the target predictive

distribution [4].

The global P. falciparum endemicity maps [1] were summaries of the posterior predictive

distribution of an annual average at year and pixels :

(SA.6)

where denotes the age range , which has been found previously to be highly

responsive to transmission intensity [6]. These annual averages were preferred to the more

standard point evaluation:

(SA.7)

because malaria transmission is known to be seasonal, meaning no particular point in time

adequately captures the overall annual transmission pattern.

Statistical model relating P. falciparum prevalence and clinical incidence The empirical prevalence-incidence model presented recently [7] modelled the

relationship between P. falciparum prevalence and observed clinical incidence as a negative

3

binomial parameterised by the average parasite rate over the survey period in the 2-10 year

old age group , and the effective length of the survey, :

(SA.8)

The function was an increasing parabolic function of average parasite rate, and the mean

incidence was assigned a Gaussian process prior. The posterior distribution of the random

function , which gave the overall relationship between average parasite rate and

incidence, was used directly in the current work. This posterior distribution is illustrated in

Figure A2.

S1.2. Volumetric Prediction and Joint Simulation To produce maps summarizing the predictive distribution of a point evaluation, such as

Eq. (SA.4), at a single point in time, it suffices to follow the procedure outlined above

independently for each pixel. That is, the predictive distributions , ,

at the pixels in the output raster grid can be sampled and summarized independently.

The computational cost of producing such a map is proportional to the number of pixels in

the raster, for large raster grids.

Producing maps summarizing temporal integrals like Eq. (SA.6) is more involved. Due to

the nonlinear link function, the integral must be approximated as a discrete sum [8] of

evaluations at a vector of time points spaced evenly between and . The pixels

can still be considered independently, but it is not sufficient to treat the time points within

independently in a given pixel. This is easy to understand by considering a limiting case: if

is very fine, the discrete sum should produce a very good approximation to the integral.

However, if is treated as independent at each point in , the sum amounts to taking the

mean of a large number of independent random variables, whose individual standard

deviations are inversely proportional to their number. The variance of this mean will be very

small: in other words, for very fine the sum is nearly determined by and . This is

clearly incorrect. Predictive samples for the discrete sum must be based on joint predictive

samples of .

The computational cost of the original P. falciparum endemicity maps [1] was proportional

to the number of pixels in the raster grid, but the constant of proportionality was larger than it

would be for maps based on point evaluations. At every pixel, and for every sample of and

4

from the MCMC trace, the covariance matrix had to be constructed

and its Cholesky decomposition had to be computed [9]. The computational costs of these

operations were proportional to the square and the cube of the length of , respectively.

However, since the length of was much smaller than the size of the dataset , these P.

falciparum endemicity maps [1] were not much more expensive to compute than maps based

on point evaluations.

The burden estimates presented in this paper are also based on predictive distributions of

integrals:

(A9)

where is the administrative unit or other geographical region in which total burden is to be

computed, is the random function mapping prevalence to clinical incidence and is

the GRUMP population surface [10] at year . Like Eq. (SA.6), this is a volumetric quantity

that can be estimated as a discrete sum.

However, the scale is massively different. The current paper and planned future modelling

work by the Malaria Atlas Project (MAP, http:www.map.ox.ac.uk), require aggregate burden

estimates and population-weighted average prevalences for any desired spatial region, from

individual pixels to continents, and over any time interval between 1985 (corresponding to

the oldest points in the MAP database) and the present. A raster grid spanning the malarious

regions of Africa at 5 × 5 km spatial resolution and monthly temporal resolution contains

approximately 345 million pixels. An covariance evaluation (where denotes that

the computational cost of this evaluation is proportional to the square of the number of

prediction locations) is completely infeasible on this scale, let alone an Cholesky

decomposition. Block circulant embedding [11] can be used to sample multivariate normal

variables in operations, but could not be adapted to our situation: reflecting the

autocovariance array along three axes requires more memory than was available and, more

importantly, due to the curvature of the Earth, the covariance matrix cannot be put in block

Toeplitz form [11].

To overcome this computational hurdle, an application-specific algorithm was designed,

which is described in full elsewhere [12]. In brief, the algorithm builds up the evaluations by

scanning over time and space, taking advantage of the empirical fact that most of the

information relevant to a particular scan-line is contained in nearby scan-lines. Although it is

much faster than standard methods, the algorithm is expensive enough that we were forced

to distribute the computation over a cluster of computers. Using this algorithm, we produced

5

500 joint simulations over , where indicates a 5 × 5 km pixel resolution

surface over the P. falciparum malarious regions of the world. The simulations and

subsequent reductions to maps and/or predictive distributions each took roughly two days of

computer time for each of the three global regions and were distributed over compute

instances on the Amazon Elastic Compute Cloud (http://aws.amazon.com/ec2).

References

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Blindern, Oslo, Noway: Norwegian Computing Centre. 64 p.

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Mathematics, Aston University. 19 p.

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Statistics. Boca Raton, Florida, U.S.A.: Chapman & Hall / CRC Press LLC.

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6. Smith DL, Guerra CA, Snow RW, Hay SI (2007) Standardizing estimates of the

Plasmodium falciparum parasite rate. Malar J 6: 131.

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relationship between Plasmodium falciparum parasite rate and clinical disease:

statistical models for disease burden estimation. Malar J 8: 186.

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10. Balk DL, Deichmann U, Yetman G, Pozzi F, Hay SI, et al. (2006) Determining global

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6

12. Gething PW, Patil AP, Hay SI (2010) Quantifying aggregated uncertainty in Plasmodium

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1

Protocol S2. A Comparison of Cartographic and Surveillance based estimates

of National Clinical Incidence.

This section provides a detailed summary of the estimated clinical burden of P. falciparum

malaria (Table S2.1), showing for each of the 87 countries defined as endemic for P.

falciparum [1], the estimated total cases with their 95% credible intervals (CIs). Sub-totals

listing estimated case numbers stratified by endemicity class are also provided. In addition,

national estimates and CIs are also graphed for each country (Figure S2), alongside an

equivalent estimate made by the World Health Organization (WHO), and published in the

World Malaria Report 2008 [2]. These estimates were revised in 2009 but data have not

been made available for all countries [3]. The WHO estimates were based on two

methodologies [4]. Data reported routinely through national health management information

systems (hereafter the WHO method) was used in approximately three-quarters of the

countries. The numbers were adjusted for non-attendance, incomplete reporting and

misdiagnosis. The remaining countries (all in Africa) used an empirical model-based estimate

that applied plausible incidence rate ranges [5] to populations at risk defined by climate

suitability for P. falciparum malaria transmission [6], as advised by the Child Health

Epidemiology Reference Group (hereafter the CHERG method).

The total global burden for 2007 estimated in this study (451 million, CI = 349-553 million)

differs substantially from that presented by the WHO using the combined methods outlined

above, who estimated a joint global burden for P. falciparum and P. vivax of 247 million

cases in 2006 (CI = 189-287) [2]. When the respective estimates for individual countries

presented in Figure S2 and Table S2.1 are examined, however, it is clear that in many cases

there is a close correspondence.

In the countries of the Americas, both approaches predict uniformly low burden, with the

largest contributions from Brazil and Haiti. Our estimated total burden for the Americas

exceeds that of the WHO by 0.4 million, or some 15%.

In the expanded Africa region, most estimates are broadly similar, with a few notable

exceptions. The pattern of differences also differs depending on the choice of method used

by the WHO. Our estimates in those countries for which the WHO used the CHERG method

tended to be larger, such that our sub-total for these countries alone exceeded that of the

WHO by 54.0 million, or 33%. The largest individual differences for these countries were in

Nigeria and the Democratic Republic of Congo, where our estimates of 73.2 and 29.1 million

cases, respectively, are both around 25% larger than the respective WHO estimates of 58.0

and 23.6 million cases. For those countries where the WHO estimates were based on the

HMIS method, the pattern was less clear, with some of our estimates substantially exceeding

the WHO's estimates (for example, in Madagascar, where our estimate of 6.7 million cases is

2

over 10 times larger than the WHO estimate of 0.6 million), and others being substantially

smaller (for example, in Kenya and Ethiopia, where our estimates of 4.9 and 5.8 million

cases are both less than half the respective WHO estimates of 11.3 and 12.4 million cases).

Because of the mixed nature of the differences, our sub-total for these HMIS countries

differed from that of the WHO by just 1.3 million cases, a relative difference of 2%. Overall,

our estimated total burden for the Africa region exceeds that of the WHO by 52.6 million, or

24%.

It is in the Central and South East Asia (CSE Asia) region that our estimates are most

consistently above those of the WHO, with our estimated total burden of 177.0 million cases

for the region being more than six times larger than the 23.9 million estimated by the WHO.

The disparity for individual countries grows in magnitude with the estimated burden, such

that our estimates of 101.5, 26.1 and 12.3 million cases for the three largest contributors,

India, Myanmar, and Indonesia, respectively, are 8.5, 5.2 and 3.9 times larger than those of

the WHO, constituting between them a difference of 121.8 million cases. Indeed, the

magnitude of these differences is such that, of the overall difference of 205 million cases in

our respective global estimates, 75% is accounted for by the CSE Asia region as a whole,

60% by just these three countries, and 45% by India alone. It is clear that reducing the

uncertainty in these global estimates and reconciling the differences between techniques will

only be possible when the data used to make the estimates in these three countries is

radically improved.

To enhance the comparability of these burden estimates, the number of clinical cases and

their appropriate confidence intervals are also summarized by WHO region (Table S2.2).

3

Figure S2. Estimated P. falciparum clinical cases by country in 2007. Red dots are the estimates (posterior means) made in this study by the Malaria Atlas

Project (MAP). Black bars illustrate the associated 95% credible interval. Blue dots and rings are the World Health Organization (WHO) estimates from the

World Malaria Report 2008 [2] using adjusted data from national health management information systems (HMIS) or the method adopted by the Child

Health Epidemiology Reference Group (CHERG), respectively. Accompanying blue bars denote the range of the lower and upper estimates. Countries are

grouped by region as defined in this study (left to right Americas, Africa+, CSE Asia) and then sorted in descending order of burden according to our

estimates.

4

Table S2.1. Estimated P. falciparum clinical cases (thousands) by country in 2007

Unstable risk PfPR2-10 ≤5% PfPR2-10 >5-<40%2 2 PfPR2-10 ≥40% Total 2 WHO3 CI ratio4

Africa + region1

Nigeria 0 (0-0) 470 (6-1,788) 16,108 (7,125-26,712) 56,626 (25,313-78,780) 73,203 (52,173-87,183) 57,506 0.48 Democratic Republic of the Congo 0 (0-0) 357 (29-880) 7,379 (4,233-10,838) 21,418 (8,337-32,257) 29,155 (19,496-36,755) 23,620 0.59 United Republic of Tanzania 0 (0-0) 580 (283-1,004) 5,843 (3,986-8,495) 6,856 (3,171-12,170) 13,279 (9,189-17,773) 11,540 0.65 Ghana 0 (0-0) 54 (0-279) 2,783 (294-5,007) 9,524 (3,991-15,076) 12,360 (8,231-15,729) 7,282 0.61 Uganda 0 (0-0) 162 (3-530) 5,526 (3,083-7,310) 6,165 (2,217-13,716) 11,853 (8,013-16,390) 10,627 0.71 Cote d'Ivoire 0 (0-0) 8 (0-70) 1,092 (115-3,062) 10,346 (6,403-12,793) 11,447 (9,291-13,264) 7,029 0.35 Mozambique 0 (0-0) 207 (35-488) 3,111 (1,275-4,512) 5,751 (1,548-10,515) 9,068 (4,930-12,460) 7,433 0.83 Burkina Faso 0 (0-0) 11 (0-88) 1,004 (15-2,982) 7,815 (3,328-9,931) 8,830 (6,110-10,142) 6,227 0.46 Cameroon 0 (0-0) 77 (1-303) 2,380 (908-4,095) 6,236 (1,297-9,909) 8,693 (4,347-11,438) 5,091 0.82 Madagascar 0 (0-0) 171 (0-411) 2,536 (1,127-3,719) 4,594 (886-9,757) 7,302 (3,228-11,127) 643 1.08 Mali 0.05 (0-0.47) 59 (3-177) 1,520 (456-2,666) 5,575 (3,165-8,324) 7,154 (5,326-8,960) 4,317 0.51 Angola 0.03 (0-0.31) 134 (2-358) 1,937 (361-3,597) 4,749 (615-9,490) 6,820 (2,734-10,055) 3,555 1.07 Niger 0.06 (0-0.62) 107 (0-327) 2,015 (885-3,043) 3,826 (432-7,740) 5,948 (3,019-8,537) 5,760 0.93 Ethiopia 0.15 (0-1.50) 1,252 (842-1,681) 3,428 (1,144-6,262) 1,103 (6-4,079) 5,783 (2,482-11,252) 12,405 1.52 Malawi 0 (0-0) 133 (11-437) 2,284 (989-3,725) 3,060 (148-6,254) 5,477 (2,519-7,844) 4,528 0.97 Guinea 0 (0-0) 36 (0-169) 1,321 (293-2,434) 3,529 (895-5,908) 4,886 (2,999-6,393) 3,766 0.69 Kenya 0.02 (0-0.17) 504 (335-826) 3,215 (2,508-3,998) 1,144 (346-2,362) 4,864 (3,872-6,109) 11,342 0.46 Sudan 0.69 (0-6.87) 706 (516-932) 2,741 (1,050-4,538) 1,249 (115-3,026) 4,696 (2,037-7,885) 5,023 1.24 Benin 0 (0-0) 26 (0-186) 967 (71-2,029) 3,228 (827-5,597) 4,221 (2,379-5,886) 3,239 0.83 Zambia 0 (0-0) 182 (58-361) 1,846 (1,143-2,687) 1,573 (256-3,414) 3,601 (1,756-5,531) 3,655 1.05 Senegal 0 (0-0) 169 (11-448) 1,962 (996-3,114) 1,268 (134-3,435) 3,398 (1,536-5,627) 1,456 1.20 Yemen 0.55 (0-5.49) 336 (107-620) 2,234 (630-3,781) 633 (0-2,244) 3,203 (1,105-5,806) 288 1.47 Togo 0 (0-0) 11 (0-100) 525 (0-1,263) 2,636 (942-4,154) 3,172 (1,951-4,223) 2,086 0.72 Sierra Leone 0 (0-0) 18 (0-166) 696 (0-1,717) 2,422 (388-4,202) 3,135 (1,633-4,358) 2,273 0.87 Chad 0.02 (0-0.16) 153 (36-299) 1,445 (723-2,237) 1,435 (133-3,723) 3,032 (1,240-4,937) 4,179 1.22 Burundi 0 (0-0) 42 (0-194) 1,100 (359-1,757) 1,207 (0-3,313) 2,350 (909-3,712) 2,271 1.19 Central African Republic 0 (0-0) 25 (0-127) 542 (83-1,081) 1,526 (435-2,863) 2,093 (1,106-3,031) 1,574 0.92 Liberia 0 (0-0) 5 (0-42) 309 (0-872) 1,767 (364-2,660) 2,080 (1,113-2,716) 1,460 0.77 Congo 0 (0-0) 9 (0-68) 345 (6-908) 1,690 (448-2,743) 2,044 (1,121-2,849) 1,332 0.85 Rwanda 0 (0-0) 46 (0-224) 1,011 (266-1,727) 969 (0-3,203) 2,026 (607-3,535) 3,251 1.45 Somalia 0.06 (0-0.55) 245 (160-401) 1,268 (774-1,807) 477 (114-1,224) 1,990 (1,272-2,800) 609 0.77 Gabon 0 (0-0) 6 (0-35) 244 (20-508) 426 (113-949) 677 (346-1,085) 387 1.09 Zimbabwe 0 (0-0) 155 (35-235) 353 (23-923) 165 (0-553) 674 (71-1,696) 350 2.41 Guinea-Bissau 0 (0-0) 16 (0-66) 273 (40-574) 280 (0-942) 568 (102-1,036) 603 1.64 Gambia 0 (0-0) 22 (0-95) 302 (65-619) 211 (0-773) 535 (144-1,010) 469 1.62 Equatorial Guinea 0 (0-0) 0 (0-0) 50 (9-113) 278 (196-379) 328 (261-422) 193 0.49 Eritrea 0.10 (0-0.96) 90 (19-170) 124 (2-617) 11 (0-149) 226 (24-840) 19 3.62 Namibia 0.04 (0-0.39) 27 (8-55) 122 (4-295) 70 (0-334) 220 (15-496) 35 2.19 Mauritania 0.04 (0-0.41) 20 (7-35) 108 (40-197) 70 (2-242) 198 (76-411) 559 1.69 Comoros 0 (0-0) 14 (0-47) 66 (0-223) 28 (0-283) 109 (2-366) 205 3.35 Botswana 0 (0-0) 16 (4-33) 44 (2-134) 14 (0-108) 75 (10-261) 7 3.38 São Tomé and Príncipe 0 (0-0) 1 (0-10) 29 (0-81) 31 (0-123) 61 (9-135) 10 2.08 Swaziland 0 (0-0) 5 (0-14) 27 (0-80) 17 (0-133) 49 (3-147) 0 2.98 Djibouti 0.04 (0-0.45) 0 (0-2) 2 (0-8) 0 (0-3) 3 (0-12) 39 3.99 Mayotte 0.03 (0-0.31) 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 0 - Cape Verde 0.03 (0-0.27) 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 0 - Saudi Arabia5 0.467 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 1 - South Africa5 2.250 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 33 -

5

Table S2.1 cont. Estimated P. falciparum clinical cases (thousands) by country in 2007

Unstable risk PfPR2-10 ≤5% PfPR2-10 >5-<40%2 2 PfPR2-10 ≥40% Total 2 WHO3 CI ratio4

Americas region1

Brazil 1.88 (0.00-18.75) 414 (198-713) 524 (22-1,874) 36 (0-289) 976 (265-2,392) 1,379 2.16 Haiti 0.00 (0.00-0.00) 462 (81-825) 465 (0-2,242) 22 (0-11) 949 (81-2,939) 165 3.01 Colombia 0.99 (0.00-9.92) 181 (32-338) 329 (0-1,838) 22 (0-306) 533 (32-2,063) 408 3.80 Ecuador 0.17 (0.00-1.66) 129 (10-306) 46 (0-313) 0 (0-0) 175 (10-583) 32 3.26 Peru 0.17 (0.00-1.73) 91 (8-242) 54 (0-382) 0 (0-0) 145 (10-547) 222 3.70 Dominican Republic 0.29 (0.00-2.89) 60 (9-115) 42 (0-238) 3 (0-0) 105 (10-336) 9 3.08 Nicaragua 0.21 (0.00-2.13) 32 (2-114) 16 (0-189) 0 (0-0) 49 (2-285) 6 5.81 Guatemala 0.53 (0.00-5.34) 19 (0-74) 12 (0-68) 13 (0-8) 44 (1-141) 141 3.10 Honduras 0.26 (0.00-2.63) 25 (1-77) 12 (0-106) 0 (0-0) 36 (1-174) 34 4.72 Venezuela 0.63 (0.00-6.31) 7 (1-17) 10 (0-70) 0 (0-0) 17 (1-82) 118 4.54 French Guiana 0.00 (0.00-0.00) 6 (1-14) 5 (0-44) 0 (0-0) 11 (1-49) 0 4.53 Bolivia 0.26 (0.00-2.61) 3 (0-15) 1 (0-10) 0 (0-0) 5 (0-28) 74 5.60 Guyana 0.05 (0.00-0.53) 2 (0-5) 0 (0-1) 0 (0-0) 2 (0-6) 58 2.83 Suriname 0.01 (0.00-0.05) 0 (0-1) 0 (0-1) 0 (0-0) 0 (0-2) 12 4.12 Belize5 0.000 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 3 -

Panama5 0.032 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 5 -

CSE Asia region1

India 60.28 (0.00-602.84) 9,964 (5,986-13,818) 51,129 (19,268-87,477) 40,374 (1,934-106,933) 101,527 (31,010-186,986) 10,650 1.53 Myanmar 0.00 (0.00-0.00) 1,075 (482-1,951) 11,920 (7,162-17,026) 13,110 (2,689-29,303) 26,105 (14,123-41,132) 4,209 1.03 Indonesia 8.61 (0.00-86.13) 1,812 (1,089-2,503) 6,780 (3,300-11,057) 3,740 (979-9,757) 12,341 (6,218-20,899) 2,518 1.18 Bangladesh 4.90 (0.00-48.99) 301 (1-1,034) 3,452 (440-7,860) 5,617 (0-19,601) 9,375 (1,216-20,636) 2,975 2.07 Philippines 2.09 (0.00-20.90) 602 (157-1,183) 2,784 (376-7,031) 2,343 (0-11,737) 5,732 (695-16,834) 124 2.81 Thailand 3.14 (0.00-31.39) 448 (257-722) 2,094 (321-3,926) 1,396 (2-5,611) 3,941 (650-9,716) 257 2.29 Viet Nam 5.48 (0.00-54.82) 634 (344-948) 2,474 (619-6,315) 607 (0-2,362) 3,721 (1,289-9,438) 70 2.18 Pakistan 6.89 (0.00-68.89) 671 (191-1,294) 1,755 (73-6,331) 831 (0-7,350) 3,264 (378-14,101) 1,499 4.19 Cambodia 0.26 (0.00-2.60) 306 (96-560) 1,755 (92-4,115) 659 (0-4,375) 2,720 (223-7,471) 262 2.66 Lao People's Democratic Republic 0.00 (0.00-0.04) 120 (40-225) 933 (295-1,739) 673 (1-2,252) 1,727 (485-3,765) 22 1.90 Malaysia 1.65 (0.00-16.48) 146 (38-278) 892 (200-1,674) 686 (4-1,992) 1,726 (424-3,455) 15 1.75 Papua New Guinea 0.00 (0.00-0.00) 105 (59-153) 795 (380 - 1,195) 721 (109 - 1,902) 1,622 (725 - 2,941) 1,508 1.37 China 2.04 (0.00-20.42) 383 (78-720) 806 (94-2,952) 218 (0-1,689) 1,410 (214-5,559) 100 3.78 Afghanistan 1.25 (0.00-12.55) 145 (58-243) 434 (11-1,335) 139 (0-1,173) 719 (109-2,256) 586 2.97 Sri Lanka 0.78 (0.00-7.77) 51 (0-128) 211 (0-848) 72 (0-747) 335 (2-1,324) 3 3.93 Solomon islands 0.00 (0.00-0.00) 9 (0-32) 92 (3-211) 166 (0-550) 267 (7-638) 106 2.36 Timor-Leste 0.00 (0.00-0.00) 21 (2-40) 125 (0-306) 106 (0-561) 252 (15-708) 529 2.75 Bhutan 0.05 (0.00-0.46) 22 (0-54) 56 (0-375) 87 (0-978) 165 (1-1,022) 16 6.19 Vanuatu 0.00 (0.00-0.00) 8 (3-16) 21 (0-73) 5 (0-37) 34 (4-118) 30 3.37 Nepal 0.62 (0.00-6.22) 11 (0-88) 4 (0-14) 0 (0-0) 16 (0-110) 31 6.92 Iran5 0.612 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 18 -

Kyrgyzstan5 0.000 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 0 -

Tajikistan5 0.006 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) 2 -

1. The regional groupings are illustrated in Figure 2A of the main text. 2. Estimated P. falciparum cases in areas of stable transmission are shown disaggregated into three policy relevant classes of endemicity, defined previously [7]. 3. These figures are estimates of malaria cases made by the World Health Organization and presented in the World Malaria Report 2008 [2]. 4. Ratio of the width of the 95% confidence intervals to the point estimate for total clinical cases per country. 5. Case numbers used directly from those countries considered to have reliable reporting systems.

6

Table S1.2. Estimated P. falciparum clinical cases (thousands) by global regions in 2007

Unstable risk PfPR2-10 ≤5% PfPR2-10 >5-<40%2 2 PfPR2-10 ≥40% Total 2

MAP regions1

Americas region 5.45 (0.00-54.50) 1,430 (965-2,096) 1,516 (116-4,490) 96 (0-1,078,545) 3,048 (1,169-6,759) Africa+ region 1.89 (0.00-18.90) 6,669 (4,939-8,425) 82,217 (70,526-96,718) 181,999 (145,203-220,361,420) 270,887 (241,127-300,578) CSE Asia region 98.05 (0.00-980.50) 16,835 (12,865-20,589) 88,512 (46,711-125,667) 71,553 (20,570-152,400,881) 176,998 (89,206-270,563)

WHO regions

AFRO 0.56 (0.00-5.69) 5,382 (3,667-7,032) 75,972 (63,484-90,003) 179,639 (142,011-216,737,879) 260,994 (231,929-288,387) EMRO 9.48 (0.00-94.80) 2,104 (1,474-2,906) 8,433 (4,881-15,676) 3,330 (885-11,690,956) 13,876 (8,203-26,029) AMRO 5.46 (0.00-54.6) 1,430 (965-2,096) 1,516 (116-4,490) 96 (0-1,078,545) 3,048 (1,169-6,759) EURO 0.00 (0.00-0.00) - - - - SEARO 78.38 (0.00-783.80) 13,705 (9,615-17,312) 75,770 (37,533-110,402) 64,504 (16,617-145,812,209) 154,057 (68,829-243,484) WPRO 11.53 (0.00-115.30) 2,314 (1,473-3,084) 10,553 (4,319-17,509) 6,079 (1,502-14,855,005) 18,958 (7,764-31,529) Global 105.40 (0.00-1,053.95) 24,935 (20,694-29,800) 172,245 (121,815-206,883) 253,648 (183,633-343,495,980) 450,933 (348,764-553,269) 1. The regional groupings are illustrated in Figure 2A of the main text. 2. Estimated P. falciparum cases in areas of stable transmission are shown disaggregated into three policy relevant classes of endemicity, defined previously [7].

7

References

1. Guerra CA, Gikandi PW, Tatem AJ, Noor AM, Smith DL, et al. (2008) The limits and intensity of Plasmodium falciparum transmission: implications for malaria control and elimination worldwide. PLoS Med 5: e38.

2. W.H.O. (2008) World malaria report 2008. WHO/HTM/GMP/2008.1. Geneva: World Health Organization. 215 p.

3. W.H.O. (2009) World malaria report 2009. Geneva: World Health Organization. 202 p. 4. Cibulskis RE, Bell D, Christophel EM, Hii J, Delacollette C, et al. (2007) Estimating trends

in the burden of malaria at country level. Am J Trop Med Hyg 77: 133-137. 5. Rowe AK, Rowe SY, Snow RW, Korenromp EL, Schellenberg JRA, et al. (2006) The

burden of malaria mortality among African children in the year 2000. Int J Epidemiol 35: 691-704.

6. Craig MH, Snow RW, le Sueur D (1999) A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today 15: 105-111.

7. Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, et al. (2009) A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 6: e1000048.

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