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Global Trends in Emerging Infectious Diseases

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Emerging infectious diseases (EIDs) are a significant burden on global economies and public health. Their emergence is thought to be driven largely by socio-economic, environmental and ecological factors, but no comparative study has explicitly analysed these linkages to understand global temporal and spatial patterns of EIDs. Here we analyse a database of 335 EID 'events' (origins of EIDs) between 1940 and 2004, and demonstrate non-random global patterns. EID events have risen significantly over time after controlling for reporting bias, with their peak incidence (in the 1980s) concomitant with the HIV pandemic. EID events are dominated by zoonoses (60.3% of EIDs): the majority of these (71.8%) originate in wildlife (for example, severe acute respiratory virus, Ebola virus), and are increasing significantly over time. We find that 54.3% of EID events are caused by bacteria or rickettsia, reflecting a large number of drug-resistant microbes in our database. Our results confirm that EID origins are significantly correlated with socio-economic, environmental and ecological factors, and provide a basis for identifying regions where new EIDs are most likely to originate (emerging disease 'hotspots'). They also reveal a substantial risk of wildlife zoonotic and vector-borne EIDs originating at lower latitudes where reporting effort is low. We conclude that global resources to counter disease emergence are poorly allocated, with the majority of the scientific and surveillance effort focused on countries from where the next important EID is least likely to originate.
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LETTERS
Global trends in emerging infectious diseases
Kate E. Jones
1
, Nikkita G. Patel
2
, Marc A. Levy
3
, Adam Storeygard
3
{, Deborah Balk
3
{, John L. Gittleman
4
& Peter Daszak
2
Emerging infectious diseases (EIDs) are a significant burden on
global economies and public health
1–3
. Their emergence is thought
to be driven largely by socio-economic, environmental and eco-
logical factors
1–9
, but no comparative study has explicitly analysed
these linkages to understand global temporal and spatial patterns
of EIDs. Here we analyse a database of 335 EID ‘events’ (origins of
EIDs) between 1940 and 2004, and demonstrate non-random glo-
bal patterns. EID events have risen significantly over time after
controlling for reporting bias, with their peak incidence (in the
1980s) concomitant with the HIV pandemic. EID events are domi-
nated by zoonoses (60.3% of EIDs): the majority of these (71.8%)
originate in wildlife (for example, severe acute respiratory virus,
Ebola virus), and are increasing significantly over time. We find
that 54.3% of EID events are caused by bacteria or rickettsia,
reflecting a large number of drug-resistant microbes in our data-
base. Our results confirm that EID origins are significantly corre-
lated with socio-economic, environmental and ecological factors,
and provide a basis for identifying regions where new EIDs are
most likely to originate (emerging disease ‘hotspots’). They also
reveal a substantial risk of wildlife zoonotic and vector-borne EIDs
originating at lower latitudes where reporting effort is low. We
conclude that global resources to counter disease emergence are
poorly allocated, with the majority of the scientific and surveil-
lance effort focused on countries from where the next important
EID is least likely to originate.
In the global human population, we report the emergence of 335
infectious diseases between 1940 and 2004. Here we define the first
temporal origination of an EID (that is, the original case or cluster of
cases representing an infectious disease emerging in human popula-
tions for the first time—see Methods and Supplementary Table 1) as
an EID ‘event’. Our database includes EID events caused by newly
evolved strains of pathogens (for example, multi-drug-resistant
tuberculosis and chloroquine-resistant malaria), pathogens that have
recently entered human populations for the first time (for example,
HIV-1, severe acute respiratory syndrome (SARS) coronavirus), and
pathogens that have probably been present in humans historically,
but which have recently increased in incidence (for example, Lyme
disease). The emergence of these pathogens and their subsequent
spread have caused an extremely significant impact on global health
and economies
1–3
. Previous efforts to understand patterns of EID
emergence have highlighted viral pathogens (especially RNA viruses)
as a major threat, owing to their often high rates of nucleotide sub-
stitution, poor mutation error-correction ability and therefore
higher capacity to adapt to new hosts, including humans
5,8,10,11
.
However, we find that the majority of pathogens involved in EID
events are bacterial or rickettsial (54.3%). This group is typically
represented by the emergence of drug-resistant bacterial strains
(for example, vancomycin-resistant Staphylococcus aureus). Viral or
prion pathogens constitute only 25.4% of EID events, in contrast to
previous analyses which suggest that 37–44% of emerging pathogens
are viruses or prions and 10–30% bacteria or rickettsia
5,8,11
. This
follows our classification of each individual drug-resistant microbial
strain as a separate pathogen in our database, and reflects more
accurately the true significance of antimicrobial drug resistance for
global health, in which different pathogen strains can cause separate
significant outbreaks
12
. In broad concurrence with previous studies
on the characteristics of emerging human pathogens
5,8,11
, we find the
percentages of EID events caused by other pathogen types to be
10.7% for protozoa, 6.3% for fungi and 3.3% for helminths (see
Supplementary Data and Supplementary Table 2 for a detailed com-
parison to previous studies).
The incidence of EID events has increased since 1940, reaching a
maximum in the 1980s (Fig. 1). We tested whether the increase
through time was largely attributable to increasing infectious disease
reporting effort (that is, through more efficient diagnostic methods
and more thorough surveillance
2,3,13
) by calculating the annual num-
ber of articles published in the Journal of Infectious Diseases (JID)
since 1945 (see Methods). Controlling for reporting effort, the num-
ber of EID events still shows a highly significant relationship with
time (generalized linear model with Poisson errors, offset by log(JID
articles) (GLM
P,JID
), F 5 96.4, P , 0.001, d.f. 5 57). This provides
the first analytical support for previous suggestions that the threat
of EIDs to global health is increasing
1,2,14
. To further investigate the
peak in EID events in the 1980s, we examined the most frequently
cited driver of EID emergence during this period (see Supplementary
Table 1). Increased susceptibility to infection caused the highest pro-
portion of events during 1980–90 (25.5%), and we therefore suggest
that the spike in EID events in the 1980s is due largely to the emer-
gence of new diseases associated with the HIV/AIDS pandemic
2,13
.
The majority (60.3%) of EID events are caused by zoonotic
pathogens (defined here as those which have a non-human animal
source), which is consistent with previous analyses of human EIDs
5,8
.
Furthermore, 71.8% of these zoonotic EID events were caused by
pathogens with a wildlife origin—for example, the emergence of
Nipah virus in Perak, Malaysia and SARS in Guangdong Province,
China. The number of EID events caused by pathogens originating in
wildlife has increased significantly with time, controlling for report-
ing effort (GLM
P,JID
F 5 60.7, P , 0.001, d.f. 5 57), and they consti-
tuted 52.0% of EID events in the most recent decade (1990–2000)
(Fig. 1). This supports the suggestion that zoonotic EIDs represent an
increasing and very significant threat to global health
1,2,7,13,14
. It also
highlights the importance of understanding the factors that increase
contact between wildlife and humans in developing predictive
approaches to disease emergence
4,6,9,15
.
Vector-borne diseases are responsible for 22.8% of EID events in
our database, and 28.8% in the last decade (Fig. 1). Our analysis
1
Institute of Zoology, Zoological Society of London, Regents Park, London NW1 4RY, UK.
2
Consortium for Conservation Medicine, Wildlife Trust, 460 West 34th Street, 17th Floor, New
York, New York 10001, USA.
3
Center for International Earth Science Information Network, Earth Institute, Columbia University, 61 Route 9W, Palisades, New York 10964, USA.
4
Odum
School of Ecology, University of Georgia, Athens, Georgia 30602, USA. {Present addresses: Department of Economics, Brown University, Providence, Rhode Island 02912, USA (A.S.);
School of Public Affairs, Baruch College, City University of New York, 1 Bernard Baruch Way, Box D-0901, New York, New York 10010, USA (D.B.).
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reveals a significant rise in the number of EID events they have caused
over time, controlling for reporting effort (GLM
P,JID
F 5 49.8,
P , 0.001, d.f. 5 57). This rise corresponds to climate anomalies
occurring during the 1990s
16
, adding support to hypotheses that cli-
mate change may drive the emergence of diseases that have vectors
sensitive to changes in environmental conditions such as rainfall,
temperature and severe weather events
17
. However, this controversial
issue requires further analyses to test causal relationships between EID
events and climate change
18
. We also report that EID events caused by
drug-resistant microbes (which represent 20.9% of the EID events
in our database) have significantly increased with time, controlling
for reporting effort (GLM
P,JID
F 5 5.19, P , 0.05, d.f. 5 57). This is
probably related to a corresponding rise in antimicrobial drug use,
particularly in high-latitude developed countries
2,7,12
.
A recent analysis showed a latitudinal spatial gradient in human
pathogen species richness increasing towards the Equator
19
, in com-
mon with the distributional pattern of species richness found in
many other taxonomic groups
20
. Environmental parameters that
promote pathogen transmission at lower latitudes (for example,
higher temperatures and precipitation) are hypothesized to drive this
pattern
19
. Our analyses suggest that there is no such pattern in EID
events, which are concentrated in higher latitudes (Supplementary
Fig. 1). The highest concentration of EID events per million square
kilometres of land was found between 30 and 60 degrees north and
between 30 and 40 degrees south, with the main hotspots in the
northeastern United States, western Europe, Japan and southeastern
Australia (Fig. 2). We hypothesize that (1) socioeconomic drivers
(such as human population density, antibiotic drug use and agricul-
tural practices) are major determinants of the spatial distribution of
EID events, in addition to the ecological or environmental conditions
that may affect overall (emerging and non-emerging) human
pathogen distribution
19
, and (2) that the importance of these drivers
depends on the category of EID event. In particular, we hypothesize
that EID events caused by zoonotic pathogens from wildlife are sig-
nificantly correlated with wildlife biodiversity, and those caused by
drug-resistant pathogens are more correlated with socio-economic
conditions than those caused by zoonotic pathogens.
We tested these hypotheses by examining the relationship between
the spatial pattern of the different categories of EID events (zoonotic
pathogens originating in wildlife and non-wildlife, drug-resistant
and vector-borne pathogens, Supplementary Fig. 2), and socio-
economic variables (human population density and human popu-
lation growth), environmental variables (latitude, rainfall) and an
ecological variable (wildlife host species richness) (see Methods).
We found that human population density was a common significant
independent predictor of EID events in all categories, controlling
for spatial reporting bias by country (see Methods, Table 1 and
Supplementary Table 3). This supports previous hypotheses that
disease emergence is largely a product of anthropogenic and demo-
graphic changes, and is a hidden ‘cost’ of human economic develop-
ment
2,4,7,9,13
. Wildlife host species richness is a significant predictor
for the emergence of zoonotic EIDs with a wildlife origin, with no role
for human population growth, latitude or rainfall (Table 1). The
emergence of zoonotic EIDs from non-wildlife hosts is predicted
by human population density, human population growth, and lati-
tude, and not by wildlife host species richness. EID events caused by
drug-resistant microbes are affected by human population density
and growth, latitude and rainfall. The pattern of EID events caused by
vector-borne diseases was not correlated with any of the environ-
mental or ecological variables we examined, although we note that
the climate variable used in this analysis (rainfall) does not represent
climate change phenomena.
a
b
c
d
Helminths
Fungi
Protozoa
Viruses or prions
Bacteria or rickettsiae
Zoonotic: unspecified
Zoonotic: non-wildlife
Drug-resistant
Non drug-resistant
Vector-borne
Non vector-borne
Zoonotic: wildlife
Non-zoonotic
Number of EID eventsNumber of EID events
Decade Decade
80
100
100
100
80
80
80
60
40
40
40
40
20
0
0
0
0
20
20
20
60
60
60
100
1940 1950 1960 1970 1990 1990
1990 1990
1980 2000 2000
2000 2000
1980
1980 1980
1970
1970 1970
1960
1960 196019501950
19501940
1940 1940
Figure 1
|
Number of EID events per decade. EID
events (defined as the temporal origin of an EID,
represented by the original case or cluster of cases
that represents a disease emerging in the human
population—see Methods) are plotted with
respect to
a, pathogen type, b, transmission type,
c, drug resistance and d, transmission mode (see
keys for details).
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Our study examines the role of only a few drivers to understand
disease emergence, whereas many other factors (for example, land
use change, agriculture) have been causally linked to EIDs
6,21
.
However, until more rigorous global data sets of these drivers become
available, data on human population density and growth act as
reasonable proxies for such anthropogenic changes. Other likely
future improvements to the model would include a more accurate
accounting for temporal and spatial ascertainment biases—for
example, the development of global spatial data sets of the amount
of funding per capita for infectious disease surveillance.
Our analyses provide a basis for developing a predictive model for
the regions where new EIDs are most likely to originate (emerging
disease ‘hotspots’). A visualization of the regression results from
Table 1 for EID events from each category (Fig. 3) identifies these
regions globally. This approach may be valuable for deciding where
to allocate global resources to pre-empt, or combat, the first stages of
disease emergence
10,14,18,22
. Our analysis shows that there is a high
spatial reporting bias for EID events (see Methods, Supplementary
Fig. 3), reflecting greater surveillance and infectious disease research
effort in richer, developed countries of Europe, North America,
Australia and some parts of Asia, than in developing regions. This
contrasts with our risk maps (Fig. 3), which suggest that predicted
emerging disease hotspots due to zoonotic pathogens from wildlife
and vector-borne pathogens are more concentrated in lower-latitude
developing countries. We conclude that the global effort for EID
surveillance and investigation is poorly allocated, with the majority
of our scientific resources focused on places from where the next
important emerging pathogen is least likely to originate. We advocate
re-allocation of resources for ‘smart surveillance’ of emerging disease
hotspots in lower latitudes, such as tropical Africa, Latin America and
Asia, including targeted surveillance of at-risk people to identify early
case clusters of potentially new EIDs before their large-scale emer-
gence. Zoonoses from wildlife represent the most significant, growing
threat to global health of all EIDs (see our data in Fig. 1, and recent
reviews
1,2,5,8,9,13,14
). Our findings highlight the critical need for health
monitoring
4,14,23
and identification of new, potentially zoonotic
pathogens in wildlife populations, as a forecast measure for EIDs.
Finally, our analysis suggests that efforts to conserve areas rich in
wildlife diversity by reducing anthropogenic activity may have added
value in reducing the likelihood of future zoonotic disease emergence.
Table 1
|
Socio-economic, environmental and ecological correlates of EID events
Pathogen type Zoonotic: wildlife Zoonotic: non-wildlife
Number of EID event grid cells 147
156 49
53
bBbB
log(JID articles) 0.34-0.37*** 1.41
1.45 0.40
0.49*** 1.49
1.63
log[human pop. density (persons per km
2
)] 0.56
0.64*** 1.75
1.90 0.88
1.06*** 2.41
2.89
Human pop. growth (change in persons per km
2
,1990
2000){ 0.09
0.45 1.09
1.56 0.86
1.31** 2.37
3.71
Latitude (decimal degrees) 0.002
0.017 1.00
1.02 0.024
0.040* 1.02
1.04
Rainfall (mm) (0.14
0.06)x10
23
1.00
1.00 (0.32
0.57)x10
23
# 1.00
1.00
Wildlife host richness 0.008
0.013** 1.01
1.01 20.015 to 20.003 0.99
1.00
Constant 29.81 to 28.78*** 213.84 to 211.73***
Pathogen type Drug-resistant Vector-borne
Number of EID event grid cells 59
64 81
88
bBbB
log(JID articles) 0.46
0.53*** 1.62
1.71 0.17
0.21*** 1.18
1.23
log[human pop. density (persons per km
2
)] 1.03
1.27*** 2.87
3.92 0.41
0.49*** 1.51
1.63
Human pop. growth (change in persons per km
2
, 1990
2000){ 1.21
1.70*** 2.73
5.06 20.08 to 0.31 0.93
1.37
Latitude (decimal degrees) 0.047
0.072** 1.04
1.07 20.015 to 0.002 0.98
1.00
Rainfall (mm) (0.35
0.61)x10
23
* 1.00
1.00 (0.10
0.28)x10
23
1.00
1.00
Wildlife host richness (20.01 to 0.16)x10
22
1.00
1.02 (0.28
0.74)x10
22
1.00
1.01
Constant 217.45 to 214.41*** 28.21 to 27.53***
Columns represent multivariable logistic regressions for EID events split according to the type of pathogen responsible. Numbers represent the range of values obtained from 10 random draws of the
possible grid squares, where b represents the regression coefficients and B represents the odds ratio for the independent variables in the model. Higher odds ratios indicate that variable value
increases have a higher likelihood of being associated with an EID event; probability value equals the median probability from 10 random draws of the possible grid squares where ***P , 0.001,
**P , 0.01, *P , 0.05, #P , 0.1. Results from each random draw are shown in Supplementary Table 3.
{ See Methods for details.
No. of EID events 1 2–3 4–5 6–7 8–11
Figure 2
|
Global richness map of
the geographic origins of EID
events from 1940 to 2004.
The
map is derived for EID events
caused by all pathogen types.
Circles represent one degree grid
cells, and the area of the circle is
proportional to the number of
events in the cell.
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METHODS SUMMARY
Biological, temporal and spatial data on human EID ‘events’ were collected from
the literature from 1940 (yellow fever virus, Nuba Mountains, Sudan) until 2004
(poliovirus type 2 in Uttar Pradesh, India) (n 5 335, see Supplementary Data for
data and sources). Global allocation of scientific resources for disease surveil-
lance has been focused on rich, developed countries (Supplementary Fig. 3). It is
thus likely that EID discovery is biased both temporally (by increasing research
effort into human pathogens over the period of the database) and spatially (by
the uneven levels of surveillance across countries). We account for these biases by
quantifying reporting effort in JID and including it in our temporal and spatial
analyses. JID is the premier international journal (highest ISI impact factor 2006:
http://portal.isiknowledge.com/) of human infectious disease research that pub-
lishes papers on both emerging and non-emerging infectious diseases without a
specific geographical bias. To investigate the drivers of the spatial pattern of EID
events, we compared the location of EID events to five socio-economic, envir-
onmental and ecological variables matched onto a terrestrial one degree grid of
the globe. We carried out the spatial analyses using a multivariable logistic
regression to control for co-variability between drivers, with the presence/
absence of EID events as the dependent variable and all drivers plus our measure
of spatial reporting bias by country as independent variables (n 5 18,307 ter-
restrial grid cells). Analyses were conducted on subsets of the EID events—those
caused by zoonotic pathogens (defined in our analyses as pathogens that origi-
nated in non-human animals) originating in wildlife and non-wildlife species,
and those caused by drug-resistant and vector-borne pathogens.
Full Methods and any associated references are available in the online version of
the paper at www.nature.com/nature.
Received 2 August; accepted 11 December 2007.
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Acknowledgements We thank the following for discussion, assistance and
comments: K. A. Alexander, T. Blackburn, S. Cleaveland, I. R. Cooke,
A. A. Cunningham, J. Davies, A. P. Dobson, P. J. Hudson, A. M. Kilpatrick,
J. R. C. Pulliam, J. M. Rowcliffe, W. Sechrest, L. Seirup and M. E. J. Woolhouse, and in
particular V. Mara and N. J. B. Isaac for analytical support. This project was
supported by NSF (Human and Social Dynamics; Ecology), NIH/NSF (Ecology of
Infectious Diseases), NIH (John E. Fogarty International Center), Eppley
Foundation, The New York Community Trust, V. Kann Rasmussen Foundation and
a Columbia University Earth Institute fellowship (K.E.J.).
Author Contributions P.D. conceived and directed the study and co-wrote the
paper with K.E.J.; K.E.J. coordinated and conducted the analyses with M.A.L., A.S.,
N.G.P. and D.B.; N.G.P. compiled the EID event database; and J.L.G provided the
mammal distribution data. All authors were involved in the design of the study, the
interpretation of the results and commented on the manuscript.
Author Information Reprints and permissions information is available at
www.nature.com/reprints. Correspondence and requests for materials should be
addressed to P.D. (daszak@conservationmedicine.org).
a
c
b
d
Figure 3
|
Global distribution of
relative risk of an EID event.
Maps
are derived for EID events caused by
a, zoonotic pathogens from wildlife,
b, zoonotic pathogens from non-
wildlife,
c, drug-resistant pathogens
and
d, vector-borne pathogens. The
relative risk is calculated from
regression coefficients and variable
values in Table 1 (omitting the
variable measuring reporting
effort), categorized by standard
deviations from the mean and
mapped on a linear scale from green
(lower values) to red (higher
values).
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METHODS
EID event definition. In this paper, we are analysing the process of disease
emergence, not just the pathogens that cause them. Therefore, we focus on
EID ‘events’, which we define as the first temporal emergence of a pathogen in
a human population which was related to the increase in distribution, increase in
incidence or increase in virulence or other factor which led to that pathogen
being classed as an emerging disease
2,4,5,8,13,15
. We chose the 1940 cut-off based on
the Institute of Medicine’s
2
examples of a currently or very recently emerging
disease, all of which had their likely temporal origins within this time period.
Single case reports of a new pathogen were not considered to represent the
emergence of a disease, and emergence was normally represented by reports,
in more than one peer-reviewed paper, of a cluster of cases that were identified in
humans for the first time, or (for previously known diseases) considered signifi-
cantly above background. Only events that had sufficient corroborating evidence
for their geographic and temporal origin were included in our analysis. We based
our data collection on the list of EIDs in ref. 5 updated to 2004. Unlike this
previous study
5
, we treated different drug-resistant strains of the same microbial
species as separate pathogens and the cause of separate EID events (for example,
the emergence of the chloroquine-resistant strain of the malaria pathogen
(Plasmodium falciparum) in Trujillo, Venezuela in 1957 and the sulphadoxine-
pyrimethamine-resistant strain in Sa Kaeo, Thailand in 1981).
Variable definitions. The biological, temporal and spatial variable definitions of
an EID event used are as follows: italic font indicates classes of the variables. (1)
‘Pathogen’, name of pathogen associated with the EID event. (2) ‘Year’ (the
earliest year in which the first cluster of cases representing each EID event was
reported to have occurred was taken where a range of years was given). (3)
‘Pathogen type’ (PathType): (i) bacterial; (ii) rickettsial; (iii) viral; (iv) prion;
(v) fungal; (vi) helminth; (vii) protozoan. (4) ‘Transmission type’ (TranType):
(0) non-zoonotic (disease emerged without involvement of a non-human host);
(1) zoonotic (disease emerged via non-human to human transmission, not
including vectors). (5) ‘Zoonotic type’ (ZooType): (0) non-zoonotic (disease
emerged via human to human transmission); (1) non-wildlife (zoonotic EID
event caused by a pathogen with no known wildlife origin); (2) wildlife (zoonotic
EID event caused by a pathogen with a wildlife origin); (3) unspecified (zoonotic
EID event caused by a pathogen with an unknown origin). (6) ‘Drug resistance’
(DrugRes): ( 0 ) event not caused by a drug-resistant microbe; and (1) event
caused by a drug-resistant microbe. (7) ‘Transmission mode’ (TranMode): (0)
pathogen causing the EID event not normally transmitted by a vector; and (1)
pathogen causing the event transmitted by a vector. (8) ‘Driver’. We classified the
most commonly cited underlying primary causal factor (or ‘driver’) associated
with the EID event according to the classes listed in refs 2, 13. We re-classified
‘Economic development and land use’ and ‘Technology and industry’ to form
more descriptive categories: ‘Agricultural industry changes’, ‘Medical industry
changes’, ‘Food industry changes’, ‘Land use changes’ and ‘Bushmeat’. (9)
‘Location’. Description of where the first cluster of cases representing each
EID event was reported to have occurred. For these descriptions, accurate
spatial coordinates (point location data) were found for 51.8% of EID events
(n 5 220) using Global Gazetteer v.2.1 (http://www.fallingrain.com/world/)
and these were assigned to corresponding one degree terrestrial spatial grids.
Some EID event locations were lesser known and only described sub-regionally
or regionally (for example, SARS in ‘‘Guangdong Province, China’’ or
enterohaemorrhagic Escherichia coli in ‘‘Peru’’). These locations were assigned
corresponding boundaries from ESRI sub-regional or regional data
24
and we
randomly selected only one grid cell from the possible grid cells to represent
each particular event. This treated these lesser known events equivalently to those
that were assigned a specific point location.
Driver definitions. Definitions of the spatial drivers used are as follows: (1)
‘Human population density’ for 2000
25
(persons per km
2
); (2) ‘Human popu-
lation growth’, calculated between 1990 and 2000
25
.We used a dummy variable
to indicate grid cells that experienced rapid growth in human population. This
variable was set to 1 for grid cells where the 1990–2000 human population
growth exceeded 25% over the decade, and was set to 0 elsewhere; (3)
‘Latitude’ (absolute latitude of the central point of each grid cell, decimal
degrees); (4) ‘Rainfall’
26
(average rainfall per year, mm); (5) ‘Wildlife host species
richness’. We calculated mammalian species richness as a proxy for wildlife host
species richness. Richness grids were generated from geographic distribution
maps for 4,219 terrestrial mammalian species
27
.
Controlling for sampling bias. For our temporal analysis, we included the
number of JID articles per year since 1945 (n
TOTAL
5 17,979 articles) as an offset
in our generalized linear model using a Poisson error structure. To control for
bias in our spatial analysis, we calculated the frequency of the country listed as the
address for every author (lead author and coauthors) in each JID article since
1973. This generated a measure of reporting effort for each country which was
matched to the one degree spatial grid for analysis and was included in the
multiple logistic regression models.
Regression analysis. Each logistic regression was repeated ten times using a
separate random draw of the EID event grids for those events where the region
reported covered more than one grid cell. The analyses are summarized in
Table 1, and given in full in Supplementary Table 3. Different random draws
can produce a different number of grid cells with events, even though the num-
ber of events does not change. For graphical purposes (that is, in Figs 2 and 3,
and Supplementary Figs 1 and 2), we display the first random draw of the EID
event grids. Human population density and number of JID articles were log-
transformed before analysis. Statistical analyses were carried out using SPSS (v.
12.0)
28
and R (v. 2.2.1)
29
. As the spatial autocorrelation (measured using Moran’s
I) in the EID event occurrence spatial grids was low (0.1), the data were assumed
to represent independent points in these analyses.
24. Environmental Research Systems Institute (ESRI). Data & Maps, Version 9.1
(Environmental Research Systems Institute, Inc., Redlands, California, 2005).
25. Center for International Earth Science Information Network (CIESIN) & Centro
Internacional de Agricultura Tropical (CIAT). Gridded Population of the World,
Version 3 (GPWv3): Population Grids (SEDAC, Columbia University, New York,
2005); available at Æhttp://sedac.ciesin.columbia.edu/gpwæ.
26. International Institute for Applied Systems Analysis (IIASA) & Food and
Agricultural Organization (FAO). Global Agro-Ecological Zones (GAEZ)
(FAO/IIASA, Rome, 2000); available at Æhttp://www.fao.org/ag/agl/agll/gaez/
index.htmæ.
27. Sechrest, W. Global Diversity, Endemism and Conservation of Mammals. Thesis,
Univ. Virginia (2003).
28. SPSS. SPSS for Windows, Version 12.0 (SPSS Inc., Chicago, 2006).
29. R Development Core Team. R: A language and environment for statistical
computing, reference index, Version 2.2.1 (R Foundation for Statistical
Computing, Vienna, Austria, 2005).
doi:10.1038/nature06536
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