Climate change and the global malaria recession.

Abstract

The current and potential future impact of climate change on malaria is of major public health interest. The proposed effects of rising global temperatures on the future spread and intensification of the disease, and on existing malaria morbidity and mortality rates, substantively influence global health policy. The contemporary spatial limits of Plasmodium falciparum malaria and its endemicity within this range, when compared with comparable historical maps, offer unique insights into the changing global epidemiology of malaria over the last century. It has long been known that the range of malaria has contracted through a century of economic development and disease control. Here, for the first time, we quantify this contraction and the global decreases in malaria endemicity since approximately 1900. We compare the magnitude of these changes to the size of effects on malaria endemicity proposed under future climate scenarios and associated with widely used public health interventions. Our findings have two key and often ignored implications with respect to climate change and malaria. First, widespread claims that rising mean temperatures have already led to increases in worldwide malaria morbidity and mortality are largely at odds with observed decreasing global trends in both its endemicity and geographic extent. Second, the proposed future effects of rising temperatures on endemicity are at least one order of magnitude smaller than changes observed since about 1900 and up to two orders of magnitude smaller than those that can be achieved by the effective scale-up of key control measures. Predictions of an intensification of malaria in a warmer world, based on extrapolated empirical relationships or biological mechanisms, must be set against a context of a century of warming that has seen marked global declines in the disease and a substantial weakening of the global correlation between malaria endemicity and climate.

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LETTERS
Climate change and the global malaria recession
Peter W. Gething1, David L. Smith2,3, Anand P. Patil1, Andrew J. Tatem2,4, Robert W. Snow5,6& Simon I. Hay1
The current and potential future impact of climate change on
malaria is of major public health interest1,2. The proposed effects
of rising global temperatures on the future spread and intensifica-
tion of the disease3–5, and on existing malaria morbidity and mor-
tality rates3, substantively influence global health policy6,7. The
contemporary spatial limits of Plasmodium falciparum malaria
and its endemicity within this range8, when compared with com-
parable historical maps, offer unique insights into the changing
global epidemiology of malaria over the last century. It has long
been known that the range of malaria has contracted through a
century of economic development and disease control9. Here, for
the first time, we quantify this contraction and the global
decreases in malaria endemicity since approximately 1900. We
compare the magnitude of these changes to the size of effects on
malaria endemicity proposed under future climate scenarios and
associated with widely used public health interventions. Our find-
ings have two key and often ignored implications with respect to
climate change and malaria. First, widespread claims that rising
mean temperatures have already led to increases in worldwide
malaria morbidity and mortality are largely at odds with observed
decreasing global trends in both its endemicity and geographic
extent. Second, the proposed future effects of rising temperatures
on endemicity are at least one order of magnitude smaller than
changes observed since about 1900 and up to two orders of mag-
nitude smaller than those that can be achieved by the effective
scale-up of key control measures. Predictions ofan intensification
of malaria in a warmer world, based on extrapolated empirical
relationships or biological mechanisms, must be set against a con-
text of a century of warming that has seen marked global declines
inthediseaseandasubstantialweakeningoftheglobalcorrelation
between malaria endemicity and climate.
Aresurgenceinfundingformalariacontrol10,theexistingefficacyof
affordable interventions, and a growing body of nationally or sub-
nationally reported declines in endemicity or clinical burden11have
engendered renewed optimism among the international malaria
research and control community. In marked contrast, however, are
modelpredictions,reportedwidelyinglobalclimatepolicydebates3,6,7,
thatclimatechangeisaddingtothepresent-dayburdenofmalariaand
willincreaseboththefuturerangeandintensityofthedisease.Inpolicy
arenas,suchpredictionscansupportscenarioanalysisorserveasacall
toaction,butthemodellingapproachesusedandtheaccuracyoftheir
predictions have not always been challenged.
Therecentpublicationofanevidence-basedmapofcontemporary
malariaendemicity8allowsanauditofchangesintheglobalepidemi-
ology of malaria since the start of the twentieth century; a period of
undoubted climatic change12. We compare this modern-day map
withthemostreliableequivalentforthepre-interventionera,around
1900 (ref. 13), and compare the magnitude of observed changes in
range and endemicity to those proposed to occur in response to
climate change and observed under existing public health interven-
tions. We use these perspectives to reassess the rationale of existing
modelling approaches toimpact assessments and thethreat posed by
future climatic changes to regional malaria control.
The only global map of pre-intervention malaria endemicity dates
from a 1968 study13(Fig. 1a) in which a major synthesis of historical
records,documentsandmapsofavarietyofmalariometricindicesfor
all four Plasmodium species was used to map parasite rate (PR—the
1Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK.2Emerging Pathogens Institute,
University of Florida, Gainesville, Florida 32610, USA.3Department of Biology, University of Florida, Gainesville, Florida 32610, USA.4Department of Geography, Universityof Florida,
Gainesville, Florida 32611, USA.5Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI – University of Oxford – Wellcome Trust Collaborative
Programme,KenyattaNationalHospitalGrounds(behindNASCOP),P.O.Box43640-00100,Nairobi,Kenya.6CentreforTropicalMedicine,NuffieldDepartmentofClinicalMedicine,
University of Oxford, CCVTM, Oxford OX3 7LJ, UK.
Risk free
Epidemic
Hypoendemic
Mesoendemic
Hyperendemic
Holoendemic
Hyperendemic
Holoendemic
Risk free
Unstable
Hypoendemic
Mesoendemic
0
–1
–2
–3
–4
–5
+1
+2
a
b
c
Figure 1 | Changing global malaria endemicity since 1900. a, Pre-
intervention endemicity (approximately 1900) as defined in ref. 13.
b, Contemporary endemicity for 2007 based on a recent global project to
define the limits and intensity of current P. falciparum transmission8.
c, Change in endemicity class between 1900 and 2007. Negative values
denote a reduction in endemicity, positive values an increase.
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proportion of individuals with malaria parasites in their peripheral
blood)stratifiedintofourendemicclasses(hypoendemic,PR,10%;
mesoendemic, PR$10% and ,50%; hyperendemic, PR$50% and
,75%; holoendemic, PR$75%). This map is the only reconstruc-
tion of historical malaria at its assumed historical peak around the
startofthetwentiethcenturyandtriangulateswellwiththeplethoraof
nationallevelmalariamapspublishedthroughoutthelastcentury(see
ref. 14 for a systematic review).
Wehaveusedrecentlycompletedworkthatdefinesthe2007limitsof
stableP. falciparum transmission and its endemicity within this range8
to generate a comparable contemporary map of P. falciparum malaria
distribution.Thismodelhasbeendescribedindetailelsewhere8andits
outputallowedforacontinuousestimateofparasiteprevalenceforthe
year 2007 (Fig. 1b) stratified into the same endemicity classes used in
the historical map13(see Methods). Comparison of the historical and
contemporary maps revealed that endemic/stable malaria is likely to
have covered 58% of the world’s land surface around 1900 (see
Supplementary Table 1) but only 30% by 2007 when P. falciparum
malariahasbecomerestrictedlargelytothetropics.Evenmoremarked
has been the decrease in prevalence within this greatly reduced range,
withendemicityfallingbyoneormoreclassesinovertwo-thirds(67%)
ofthecurrentrangeofstabletransmission(Fig.1c).Thecontemporary
map indicates thatholoendemicP. falciparum malaria isnow rare and
limited to patches in West Africa totalling around 140,000km2. The
Americas are entirely hypoendemic for P. falciparum, as are very large
sectionsofcentralandsoutheastAsiaandsubstantialswathesofAfrica.
When empirical relationships linking the current spatial distribu-
tions of surface temperature and malaria are extrapolated to predict
future changes in disease range or intensity under scenarios of rising
global temperatures, a necessary assumption is that all other factors
either remain constant or have a relatively negligible effect. During a
century in which global temperature increases have been unequi-
vocal12,15,wehavedocumentedamarked,globaldecreaseintherange
and intensity of malaria transmission. This suggests that such an
assumption would not have been valid for predicting the response
of malaria to the warming climate of the last 100years. A second
assumption is that the nature of the link between climate and the
global distribution and intensity of malaria is effectively immutable:
anempiricalclimate–malariarelationshipobservedatonetimeperiod
will be preserved even under changing climate and disease scenarios.
However, a comparison of global-scale climate patterns with the his-
torical and contemporary patterns of malaria endemicity presented
here indicateda decoupling of thegeographical climate–malaria rela-
tionship over the twentieth century (see Supplementary Information
foradditionalexplanationandresultsofthisanalysis),indicatingthat
non-climatic factors have profoundly confounded this relationship
over time. Contemporary endemicity maps therefore provide a poor
baseline for empirically-based predictions of future climate effects.
Linking increasing temperatures to changes in malaria epidemi-
ologyisjustifiedtheoreticallybyknownbiologicaleffectsondifferent
life-cycle stages of the Anopheles vector and Plasmodium parasite16.
Such effects do not act in isolation, however, and empirical predic-
tions are only credible if the role and relative influence of non-
climaticfactorsisconsidered.Asimpleinterpretationoftheobserved
global recession in malaria since 1900 is that non-climatic factors,
primarilydirectdiseasecontrolandtheindirecteffectsofacenturyof
urbanization and economic development, although spatially and
temporally variable, have exerted a substantially greater influence
on the geographic extent and intensity of malaria worldwide during
the twentieth century than have climatic factors. This simple infer-
ence is consistent with other studies that have reviewed the historical
climatic and anthropogenic forces acting on malaria17and has
important implications for the debate on the importance of climate
change in determining future malaria scenarios.
Biological modelling provides an alternative to empirical ap-
proaches, enabling the magnitude of potential disease responses to
future climate scenarios to be estimated directly16,18,19and then
compared formally with observed or predicted effects from non-
climatic influences. The P. falciparum basic reproductive number,
PfR0, quantifies the expected number of secondary cases in a non-
immune population resulting from a single new infection and is the
appropriate metric forcomparing therelativemagnitude ofdifferent
effectsontheunderlying intensityoftransmission20.Linkedclimate–
biological models that simulate changes to PfR0under different
climate-change scenarios have proposed effect sizes of up to three
(Table 1), that is, a tripling of the reproductive number16,18,19. To
Table 1 | Comparison of magnitude of changes (effect size) in the basic
reproductive number in relevant observational or predictive studies
Effect size (relative
change in R0)
Study
Estimated changes 1900–2007
Proportion of 1900 endemic world by
area*
13%
12%
18%
57%
Changes predicted under climate change
Spatially aggregated mean change by
,2050{1
P. falciparum
P. vivax
Range of local changes by ,20501I"
Range of local changes by ,20801I
Range of local changes by ,20501I"
Example effect sizes for interventions
predicted via biological modelling
ACTs (compared to failing pre-ACT
treatment)
ITNs (at 40–60% effective coverage)
ITNs 1 LCI (both at ‘moderate’ coverage
levels)
Example effect sizes observed
concurrently with increased intervention
coverage (primary interventions in use)
Western Kenya{{ (ITNs, ITNs 1 LCI)
Sa ˜o Tome ´ and Principe11 (IRS 1 ITNs 1
ACT 1 IPTp)
Bioko Island"" (IRS 1 ACT)
Southern Mozambiqueqq (IRS 1 ACT)
Zanzibar{{{ (ITNs 1 ACT); 0–5 yr,
6–14 yr
4#1{
41–10
410–100
4.100
This study
This study
This study
This study
Ref. 16
31.27 (1.16–1.74)
31.23 (1.15–1.39)
30–2
30–2#
30–3
Ref. 16
Ref. 19
Ref. 18
41.1–1.8q
Ref. 21
45–15**
415–25{{
Ref. 22
Ref. 23
47.2{{,426.4{{
45.1II
Ref. 25
Ref. 26
42.9##
478.8***
41.6{{{,41.8{{{
Ref. 27
Ref. 28
Ref. 29
ACT, Artemisinin-based combination therapy; EIR, entomological inoculation rate; IPTp,
intermittent preventive treatment in pregnancy; ITN, insecticide-treated bed net; LCI, larval
control intervention.
*Percentages do not sum to 100 because of rounding. {Consists of 11% of land area with no
evidence of change, and 2% with evidence of increased transmission. {The available results
relate to the combined Africa, South-East Asia, and Central and South America regions. Given
are central modelled values and uncertainty intervals associated with plausible ranges of input
biological parameters. 1Results obtained using the MIASMA linked climate-biological model,
underthreefutureclimatescenarios,andonlythelargestpredictedchangesareshownhere.IIn
theoriginalstudy,results werenot presentedfor areaswith verylowbaselinepotential toavoid
using infinitesimal values as denominators in the comparison. "Results apply to both P.
falciparum and P. vivax. #Range of predicted changes included a ’.2’ category but no further
details provided. qStudy reported predicted changes in parasite rate from five real-world
baselineendemicitysettingsunderfailingtreatmentregimesbasedonpre-ACTmonotherapies
(Table 4 in ref. 21). We converted transitions in parasite rate into transitions in R0to estimate
effect size. **Study modelled effect size as a continuous function of ITN effective coverage.
Values presented based on Fig. 1 in ref. 22. {{Study reported effect sizes in terms of EIR, which
were interpreted directly as R0effect sizes. {{Results from a control trial. 11Integrated malaria
control effort involving mass intervention coverage complemented with health system
strengthening. Reported decline relates to period 2005–2007. IIStudy reported reduction in
communityparasiteratefrom30.5%to2.1%followingexpandedcontrolefforts.Weconverted
this into transitions in R0to estimate effect size. ""Integrated malaria control effort involving
mass IRS administration, improved case management with ACT and strengthened health
system surveillance and diagnostics. Reported decline relates to period 2004–2005. ##Study
reported reduction in community parasite rate from 46% to 31% following expanded control
efforts. We converted this into transitions in R0to estimate effect size. qqIntegrated malaria
control effort involving IRS administration, improved case management with ACT and
strengthened health systems. Reported decline relates to period 1999–2005. ***Study
reported reduction in community parasite rate from 65% to 4% in study zone with longest
running intervention coverage (Zone 1, values taken from Table 1 of ref. 28). We converted this
into transitionsin R0to estimateeffectsize. {{{Integrated malariacontrol effort involvingscale
up oflong-lastingITNsfrom 10%to90%coverageofchildren,switchfrom chloroquinetoACT
asfirstandsecondlinetherapeuticandstrengthenedsurveillanceandhealthsystems.Reported
decline relates to period 2003–2005. {{{Study reported reduction in parasite rate in children
0–5yrfrom9%to0.3%and inchildren6–14yr from12.9%to1.7%followingexpandedcontrol
efforts. We converted this into transitions in R0to estimate effect size.
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place the proposed magnitude of these effects in context, we com-
pared them to the observed reductions in global endemicity over the
past century. To allow such a comparison, a simple P. falciparum
transmission model20was used to translate the historic and contem-
porary estimated endemicity classes into approximate values of the
reproductive number (see Methods). Using this model, the hypoen-
demic class (central PR value55%) was interpreted as representing
an approximate R0value of 1.3; holoendemic (central PR530%) as
5.5; mesoendemic (central PR563%) as 87.7; and hyperendemic
(centralPR588%)as175.6.Theseconversionsallowedtheobserved
changesbetweenhistoricalandcontemporaryendemicitytoberecast
in terms of proportional changes (effect sizes) in the reproductive
number and summarized simply into orders of magnitude to reflect
theirlikelyprecision.Inthiswaywefoundthat,ofthe66millionkm2
of the Earth’s surface thought to have sustained stable/endemic
malaria in 1900, 12%, 18% and 57% had exhibited proportional
decreases in the reproductive number of up to one, between one
and two, and greater than two orders of magnitude, respectively;
11% had shown no evidence of change; and 2% had shown evidence
of an increase in the reproductive number by 2007 (see Supplemen-
tary Information for additional details of methods and results).
Although imperfect, this simple comparison illustrates that despite
warming global temperatures12, the combined natural and anthro-
pogenic forces acting on the disease throughout the twentieth
century have resulted in the great majority of locations undergoing
a net reduction in transmission between one and three orders of
magnitude larger than the maximum future increases proposed
under temperature-based climate change scenarios (Table 1).
Iftheeffectsofclimatechangearetoreversetheobserveddeclining
trend in malaria endemicity, theymust exceed the collective counter-
acting effects of continuing economic development and increasing
control efforts. Whereas the former is hard to quantify, the potency
ofcurrentlyavailablecontrolandinterventiontoolshasbeenassessed
theoreticallyusingbiologicalmodels21–23andalsoevaluatedinpractice
whenchangesinlocaltransmissionhavebeenmeasuredconcurrently
withaggressivemalariacontrolinitiatives24–29.Wetranslatedexamples
of predicted and observed changes in endemicity caused by control
efforts into PfR0effect sizes and compared these directly with effect
sizes proposed under climate change. This indicated that suites of
interventions at high coverage can achieve effect sizes exceeding two
orders of magnitude (Table 1). When compared to the substantially
smallerproposedmagnitudeofclimate-inducedeffects,animportant
and simple inference is that the latter can be offset by moderate
increases in coverage levels of currently available interventions.
Various sources of uncertainty exist in the inputs and methodo-
logiesusedinthisstudy.Theproposedlevelsofhistoricalendemicity13
areplausiblewhentriangulatedagainstothervaluesreportedfromthe
pre-intervention era (for example, see refs 28, 30 and 31), but the
relatively crude categorization of all-cause malaria endemicity strata
and the cartographic approach used preclude a more formal quan-
tification of the precision in the global P. falciparum endemicity
declineswereport,andthesubsequentconversionsintoP.falciparum
reproductive number effect sizes are approximate. However, the
key comparisons we present between observed declines, proposed
temperature-driven increases, and the impact of available counter-
measuresrelyonourestimatesbeingaccurateonlytowithinoneorder
of magnitude. We have compared two snapshots of estimated global
endemicityseparatedbyaperiodof100yearsduringwhichchangesin
global temperatures have been accelerating, with the largest observed
warming occurring in recent decades12. The progression of the reces-
sion in global malaria is less well measured, and several periods of
decline and resurgence are known to have occurred associated with,
for example, periods of coordinated large-scale control efforts, the
introduction and subsequent failure of successive therapeutics, or
breakdowns in public health infrastructure associated with conflict
or political upheaval. There is, however, no evidence of a systematic
upturn in malaria endemicity concurrent with the warming trend of
recent decades, while a growing body of evidence points to recent
regional declines in malaria morbidity or mortality in areas achieving
sustainedinterventioncoverage11,26–29.Wehaveinterpretedtheresults
presentedin this study ata globalscale.Any effects of risingtempera-
tures on malaria transmission would most likely be extremely geo-
graphically variable, with most modelling studies suggesting that the
fringesofthecurrentdiseaseextentwouldbemostsensitivetowarm-
ing5,16,19.However,thesemorelocalpredictionsarethemselveslargely
underminedbycurrentobservations;themarginaltransmissionzones
are the places from which the largest declines in malaria transmission
ormorbidityarebeingreported,concurrentwithimprovedinterven-
tion coverage26–29and consistent with biological modelling studies
that indicate a greater theoretical impact on endemicity in areas of
lower baseline transmission32.
Inanerawhentheinternationalcommunityhasbeenemboldened
to provide guidelines for malaria elimination it is necessary to main-
tainthecorrectperspectiveonthefutureimpactofclimatechangeon
malaria epidemiology and by implication its malaria public health
importance. Thequantification ofaglobal recession intherange and
intensity of malaria over the twentieth century has allowed us to
review the rationale underpinning high-profile predictions of a cur-
rent and future worsening of the disease in a warming climate. It
suggeststhatthesuccessorfailureofoureffortsagainsttheparasitein
the coming century are likely to be determined by factors other than
climate change.
METHODS SUMMARY
The historical malaria endemicity map13was scanned from the original publica-
tion, digitized on-screen and rasterized to a 535km grid. The map of con-
temporary malaria endemicity was generated from a recently defined model8of
age-standardisedP.falciparumparasiterate, Pf PR2-10. Usingamodel-basedgeo-
statistical framework, the underlying value of Pf PR2-10at each location was
modelled for the year 2007 as a transformation of a space-time Gaussian process
(GP), with the number of P. falciparum-positive individuals in each survey
modelledasabinomialvariategiventheunobservedage-standardisedprevalence
surface. The GP was parameterised by a mean component (a linear function of
time and urban–peri-urban–rural status) and a space-time covariance function
which was spatially anisotropic, used great-circle distance to incorporate the
curvature of the earth, and included a periodic temporal component to capture
seasonality. Bayesian inference was implemented using Markov chain Monte
Carlo and direct simulation to generate posterior predictive samples of the
2007 annual mean prevalence surface and to assign each pixel to the endemicity
class with the highest posterior probability of membership.
Predicted Pf PR2-10values at each pixel were converted into approximate
values of the P. falciparum basic reproductive number, PfR0, using a model that
assumes new infections are acquired and clear independently at a constant rate
and that biting is heterogeneously distributed in a population such that relative
biting rates follow a Gamma distribution. Pf PR was empirically related to the
Pf EIRusingalog-linearrelationshipbasedon91pairedobservations.Pf EIRcan
be inferred from Pf PR (X) by inverting the formula20,22. It follows that:
???ð1 ? XÞ?a? 1???
where k is the net infectiousness of humans, that is, the probability that a
mosquito will become infected after biting a human, c is the probability that a
mosquito will become infected after biting a non-immune infectious human,
and S is the stability index.
R0¼
a
cð1 þ SkÞð1 þ aÞ
k
Full Methods and any associated references are available in the online version of
the paper at www.nature.com/nature.
Received 3 February; accepted 16 April 2010.
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Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.
Acknowledgements We thank A. Bibby, H. C. J. Godfray, G. D. Shanks and
G. R. W. Wint for comments on the manuscript. S.I.H. is funded by a Senior
Research Fellowship from the Wellcome Trust (#079091) that also supports
P.W.G. and previously A.J.T. R.W.S. is funded by a Principal Research Fellowship
fromtheWellcomeTrust(#079080)thatalsosupportsA.P.P.D.L.S.andA.J.T.are
supportedbyagrantfromtheBillandMelindaGatesFoundation(#49446).D.L.S.
and S.I.H. also acknowledge funding support from the RAPIDD program of the
Science & Technology Directorate, Department of Homeland Security, and the
Fogarty International Center, National Institutes of Health. 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.
Author Contributions S.I.H. conceived the research. P.W.G. and S.I.H. drafted the
manuscript. P.W.G. led, and A.P.P., D.L.S., A.J.T. and R.W.S. contributed to, the
analyses. All authors discussed the results and contributed to the revision of the
final manuscript.
Author Information Reprints and permissions information is available at
www.nature.com/reprints. The authors declare no competing financial interests.
Readers are welcome to comment on the online version of this article at
www.nature.com/nature. Correspondence and requests for materials should be
addressed to S.I.H. (simon.hay@zoo.ox.ac.uk) or P.W.G
(peter.gething@zoo.ox.ac.uk).
NATURE|Vol 465|20 May 2010
LETTERS
345
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METHODS
Generating comparable historical and contemporary endemicity maps. The
historical malaria endemicity map13was scanned from the original publication
and geo-referenced using ERDAS Imagine 8.5 (Leica Geosystems GIS &
Mapping). The map was then digitised on-screen with MapInfo Professional
7.0 (MapInfo), and rasterized to a 535 km grid. The map of contemporary
malariaendemicitywasgeneratedfromarecentlydefinedmodel8ofP.falciparum
infectionprevalence withinthepreviouslydefinedlimits ofstabletransmission33.
The underlying value of P. falciparum parasite rate in the 2–10-year age cohort,
PfPR2?10ðxiÞ, at each location xiwasmodelled fortheyear 2007 asa transforma-
tiongð:Þofaspace-timeGaussianprocessf ðxi;tiÞwithmeanmandcovarianceC
superimposed with additional aspatial (random) variation eðxiÞ, represented as
Gaussian with zero mean and variance V. The number of P. falciparum positive
individuals, Nþ
i, from the total sample of Niin each survey was modelled as a
conditionallyindependentbinomialvariategiventheunobservedunderlyingage-
standardizedPfPR2?10value34.Thespace-timemeancomponentm wasmodelled
asalinearfunctionoftime,t,andurban–peri-urban–ruralstatus(denotedbythe
indicator variables1u1(x),1p1(x)). The mean component was therefore defined
as:
m ¼ bxþ btt þ bu11u1ðxÞ þ bp11p1ðxÞ
where bxdenotestheintercept.Eachparasiteratesurveywasreferencedtemporally
usingthemid-point(indecimalyears)betweentherecordedstartandendmonths.
Covariance between spatial and temporal locations was modelled using a spatially
anisotropic space-time covariance function C with a periodic component (wave-
length512 months) added to the time-marginal covariance model to capture
seasonality35:
Cðxi;ti;xj;tjÞ ¼ t2cð0ÞðDxÞcðDtÞKcðDtÞðDxÞ
2cðDtÞ?1CðcðDtÞ þ 1Þ;
1
½
cðDtÞ ¼
2r þ 2ð1 ? rÞ ð1 ? uÞe?jDtj=wt þ ucosð2pDtÞ
Dt ¼ jti? tjj
whereKcisthemodifiedBesselfunctionofthesecondkindoforderc,andC isthe
gamma function36,37. The effect of the curvature of the earth on point-to-point
separations, and a mechanism for spatial anisotropy, were incorporated by com-
putingthespatialdistancebetweenapairofpointsxiandxjasgreat-circledistance
DGCðxi;xjÞ on a flexible ellipsoid. Bayesian inference was implemented using
Markov Chain Monte Carlo to generate samples from the posterior distribution
oftheGaussianfieldf ðxi;tiÞateachdatalocationandoftheunobservedparameters
of the mean, covariance function and Gaussian random noise component and
direct simulation was then used to generate samples from the 2007 annual mean
of the posterior distributionoff ðxi;tiÞ ateach predictionlocationacrossthe same
template535kmgridasthat usedforthe historicalmap.Modeloutputtherefore
consisted of samples from the posterior predictive distribution of the 2007 annual
mean PfPR2-10at each grid location. We assigned each pixel to the historical
?;
endemicity class with the highest posterior probability of membership, identified
as theclass containingthelargest proportionof posteriorsamples. Pixels modelled
as being at unstable or no risk were assigned directly because these classifications
weredeterministic.Theage-standardization(2–10-year-olds)matchedthatusedin
the historical map13except for the holoendemic class (Pf PR.70%) which the
authors defined as relating to prevalence in one-year-olds.
Translating predicted endemicity into approximate values of PfR0. We have
developedafunctiontoestimatethePfR0fromthePf PRonthebasisofamodel
that assumes that new infections are acquired and clear independently at a
constantrateandthat bitingisheterogeneouslydistributedinapopulationsuch
that relative biting rates follow a Gamma distribution with mean 1 and variance
a20,22,30.Thetransformationwasdevelopedwith91pairedestimatesofthePf EIR
andthePf PRinAfricanchildren,standardizedfollowinganalgorithmdescribed
inref.31.Pf PRisempiricallyrelatedtothePf EIRinthese91pairedestimatesby
a log-linear relationship38, or by a simple formula that describes the steady state
of a malaria transmission model20. Pf EIR can be inferred from Pf PR ðXÞ by
inverting the formula20,22. It follows that:
???ð1 ? XÞ?a? 1???
wherek is thenet infectiousness of humans,the probability that a mosquitowill
become infected after biting a human, c is the probability that a mosquito will
become infected after biting a non-immune infectious human, and S is the
stability index.
The transformation from Pf PR to PfEIR gives unsatisfactory estimates of
Pf EIR when Pf PR exceeds approximately 65%. At these high values of Pf EIR
andPf PR,itisgenerallypossibletofindavaluesuchthatthefunctionfitsexactly.
ThesevaluesarestatisticallysignificantlyandnegativelycorrelatedwithPf PR,by
the relationship a ¼ 9:292 ? 8:035X.To make the transformation, we take
a ¼ minð4:2;9:292 ? 8:035XÞ. To make the transformation from Pf PR to net
infectiousness, we assume c ¼ 0:1 consistent with ref. 39, and we use the formula
described elsewhere20,22, assuming no immunity. We also assume that S ¼ 1,
althoughk,1,andS isgenerallylowerthanapproximately5,so1,11Sk,1.5.
R0¼
a
cð1 þ SkÞð1 þ aÞ
k
33. Guerra,C.A.etal.ThelimitsandintensityofPlasmodiumfalciparumtransmission:
implications for malaria control and elimination worldwide. PLoS Med. 5, e38
(2008).
34. Diggle, P. & Ribeiro, P. J. Model-based Geostatistics (Springer, 2007).
35. Stein, M. L. Space-time covariance functions. J. Am. Stat. Assoc. 100, 310–321
(2005).
36. Antosiewicz, H. A. in Handbook of Mathematical Functions (eds Abramowitz, M. &
Stegun, I. A.) 435–479 (Dover Publications, 1964).
37. Davis,G.M.inHandbookofMathematicalFunction(edsAbramowitz,M.&Stegun,
I. A.) 253–295 (Dover Publications, 1964).
38. Hay, S. I., Guerra, C. A., Tatem, A. J., Atkinson, P. M. & Snow, R. W. Urbanization,
malariatransmissionanddiseaseburdeninAfrica.NatureRev.Microbiol.3,81–90
(2005).
39. Killeen, G. F., Ross, A. & Smith, T. Infectiousness of malaria-endemic human
populations to vectors. Am. J. Trop. Med. Hyg. 75, 38–45 (2006).
doi:10.1038/nature09098
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SUPPLEMENTARY INFORMATION
1
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doi: 10.1038/nature09098
1
Supplementary Information
A: The weakening geographical relationship between climate and
malaria endemicity 1900-2007
Temperature and rainfall are two climatic variables known to assert fundamental influence
on local environmental suitability for malaria transmission, through effects on vector and
parasite longevity and reproduction1. A 0.5° x 0.5° (approx. 55 x 55 km at the equator) spatial
resolution gridded climatology2was used to create mean temperature and rainfall surfaces. The
climatology is created by interpolating meteorological station data for the period 1901 to 2002
to create monthly surfaces which can be averaged temporally to create synoptic annual means.
These synoptic temperature and rainfall surfaces were imported into a geographical information
system (ArcGIS 9.2, ESRI Inc, Redlands CA, USA) and overlaid using a common projection
(geographic projection using the Clarke 1866 datum). The centroid position of each 0.5° x 0.5°
climatology pixel was defined and converted into a set of point locations. These point locations
were then used to extract class membership values from the historical (c. 1900) Lysenko3and
contemporary 20074endemicity surfaces within the GIS. This procedure resulted in four values
(synoptic annual mean temperature and rainfall, and malaria endemicity class as defined by
Lysenko and by the contemporary surface) for every 0.5° x 0.5° pixel across the Earth's land
surface (67,421 locations). No values were extracted for Antarctica. Extracted values were then
imported into the R software package5and boxplots were generated to summarise the
distribution (0.1, 0.25, 0.5 (median), 0.75, and 0.9 quantiles) of the rainfall and temperature
climatology variables within regions stratified by endemicity class as defined by Lysenko and
by the contemporary endemicity map.
When the climate variables were aggregated according to the historical malaria endemicity
strata, a monotonic trend was observed for both variables: each successively higher class of
endemicity was associated with increasing temperatures and rainfall (Fig. S1, left column),
consistent with theoretical relationships between malaria transmission and climate in non-
intervention settings1. These relationships all but disappear, however, when the contemporary
endemicity map is compared with the same climatology (Fig. S1, right column). This simple

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observation illustrates that these two key climatic drivers of environmental suitability for
malaria have little explanatory power when analysed against a distribution significantly reduced
from the hypothesised fundamental niche6of the species and with an endemic level modified
extensively and non-uniformly by control and development across the globe. These analyses
were repeated using averaged 1900-1921 and 1980-2001 climatologies with the historical and
contemporary malaria distributions, respectively, and the resulting boxplots were almost
identical (available on request).
B: Estimating changing endemicity in terms of PfR0effect size
The main manuscript (Fig. 1) presents maps showing the presumed historical (c. 1900)
and modelled contemporary (2007) global endemicity of malaria, and the differences between
the two maps, stratified into classes defined in units of parasite prevalence. The predicted land
area covered by each endemicity class in 1900 and in 2007 is summarised in Table S1.In order
to compare the observed changes in endemicity between these time periods with hypothesised
changes due to climate change and modelled or observed changes due to contemporary control
measures, it was necessary to translate both maps into approximate units of PfR0, the P.
falciparum basic reproductive number. The Methods section explains the use of a simple P.
falciparum transmission model to estimate a value of PfR0corresponding to the mid-value of
each endemicity class. The converted values, and magnitude of transitions between classes are
summarised in Table S2.
Using these conversion values, maps of PfR0were made corresponding to both historical
(Fig. S2a) and contemporary (Fig. S2b) endemicity. These two maps were overlaid in a
geographical information system (GIS) (ArcGIS 9.2, ESRI Inc, Redlands CA, USA) and the
relative change in PfR0between the historic and contemporary map was calculated for each 5 ×
5 km pixel. These relative changes were summarised as either areas of increase; areas of no
change; and areas of decrease of between zero and one, one and two and greater than two orders
of magnitude (Fig. S2c). The land area represented by each class was then calculated in the GIS
by reprojecting the maps to an equal-area projection (Mollweide projection), and then

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summarised as a fraction of the historically endemic world, i.e. all areas with a positive PfR0in
1900 (Table S3).
Supplementary References
1Craig, M., Snow, R. W., & Le Sueur, D. A climate-based distribution model of malaria
transmission in Sub-Saharan Africa. Parasitol. Today 15, 105-111 (1999).
2Mitchell, T. D. & Jones, P. D. An improved method of constructing a database of monthly
climate observations and associated high-resolution grids. Int. J. Climatol. 25, 693-
712 (2005).
3Lysenko, A. J. & Semashko, I. N. Geography of malaria. A medico-geographic profile of
an ancient disease [in Russian], in Itogi Nauki: Medicinskaja Geografija (ed A. W.
Lebedew) 25-146 (Academy of Sciences, Moscow, 1968).
4Hay, S. I., et al. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS
Med. 6, e1000048 (2009).
5R Development Core Team, R: a language and environment for statistical computing (R
Foundation for Statistical Computing, URL: http://www.R-project.org, Vienna,
Austria, 2009).
6Southwood, T. R. E. Habitat, templet for ecological strategies? Presidential address to
British Ecological Society, 5 January 1977. J. Anim. Ecol. 46, 337-365 (1977).
7Guerra, C. A., et al. The limits and intensity of Plasmodium falciparum transmission:
implications for malaria control and elimination worldwide. PLoS Med. 5, e38
(2008).

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Supplementary Figures
Figure S1. Characteristics of synoptic climatic variables in regions defined by
different classes of malaria endemicity. Mean annual temperature (a) and rainfall (b)
for period 1901 to 20022stratified by endemicity class. Left panels: pre-intervention
endemicity (c. 1900) as defined by Lysenko3; Right panels: Contemporary endemicity
(2007) based on a recent global project to define the limits7and intensity of current P.
falciparum transmission4. For each endemicity class, the distribution of temperature or
rainfall values is summarised by the median (heavy line), inter-quartile range (coloured
box), and the range of the 0.1 to 0.9 quartiles (outer bars).

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Figure S2. Maps estimating the P. falciparum basic reproductive, PfR0.
Estimated values of PfR0corresponding to: (a) historical and (b) contemporary
malaria endemicity classes, and (c) the magnitude of decrease between those
two periods. See text for more details.

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Supplementary Tables
Table S1 The changing global area of malaria subdivided by endemicity
class
Lysenko (c.1900)Contemporary (2007)
km2(%)1
km2(%)1
No risk
57.06 (42.35)94.94 (70.45)
Epidemic / unstable
12.01 (8.91)11.00 (8.16)
Hypoendemic
22.61 (16.78)17.06 (12.66)
Mesoendemic
23.27 (17.27)8.83 (6.55)
Hyperendemic
14.77 (10.96) 2.77 (2.06)
Holoendemic
5.03 (3.73)0.15 (0.11)
1. Area figures are in millions and the percentage figures express the proportion of the total
global land area occupied by that endemicity class. All values exclude Antarctica.

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Table S2 Conversion of endemicity classes (PR) into R0and magnitude
of change associated with transitions between historic and
contemporary classes
Lysenko endemicity classHypoMesoHyperHolo
PR (centre value)
5% 30% 63%88%
R0equivalent
1.35.587.7175.6
Proportional changes in R0
associated with transitions from
historical (columns) to
contemporary (rows) endemicity
classes. Parentheses show order
of magnitude of change
Hypo-
(-)
÷ 4.2
(0-1)
÷ 67.4
(1-2)
÷ 135.0
(2-3)
Meso × 4.2
(0-1)
-
(-)
÷ 15.9
(1-2)
÷ 31.8
(1-2)
Hyper × 67.4
(1-2)
× 15.9
(1-2)
-
(-)
÷ 2.0
(0-1)
Holo× 135.0
(2-3)
× 31.8
(1-2)
× 2.0
(0-1)
-
(-)

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Table S3 Global land area associated with different magnitude changes
in PfR0between 1900 and 2007.
Change in PfR01900 - 2007
Area ('000 km2)Proportion of 1900 endemic world
Increase
No change
Decrease 0-1 orders of magnitude
Decrease 1-2 orders of magnitude
Decrease >2 orders of magnitude
1,371
7,041
8,043
12,009
38,096
2%
11%
12%
18%
57%