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Risk Analysis DOI: 10.1111/risa.13410
Spatial Quantification of the Population Exposed to
Cryptococcus neoformans and Cryptococcus gattii Species
Complexes in Europe: Estimating the Immunocompetent
and HIV/AIDS Patients Under Risk
Alberto J. Alaniz ,1,2 ,∗Jorge G. Carvajal,1Mario A. Carvajal,1Massimo Cogliati,3
and Pablo M. Vergara2
Cryptococcus is an important fungal pathogen worldwide, causing serious clinical manifesta-
tions that can affect immunocompetent patients and can be particularly severe for immuno-
compromised patients. The Cryptococcus gattii s.s. (AFLP4/VGI), Cryptococcus tetragattii
(AFLP/VGIV), Cryptococcus neoformans,andCryptococcus deneoformans have been iso-
lated from both clinical and environmental sources in Europe. We aim to quantify the people
in Europe and the entire Mediterranean area who are under risk associated with each of the
three fungal pathogens in a spatially explicit way, generating a series of maps and population
statistics per country. Niche modeling was applied to estimate the potential distribution of
each fungal pathogen, then these models were overlapped with a map of population density
to estimate risk levels. The potential number of people per risk level and per country was
quantified using a map of population count per pixel. Prevalence of HIV per country was
also included in the analysis to quantify the HIV-infected population under potential risk.
People under risk associated with exposure to C. gattii species (C. gattii s.s. and C. tetragat-
tii) reached 137.65 million, whereas those exposed to C. neoformans and C. deneoformans
were 268.58 and 360.78 million people, respectively. More than a half million HIV-infected
patients are exposed to each of the two species of the C. neoformans species complex, and
more than 200,000 to the C. gattii species complex. The present results can be useful for public
health planning by European governments, focusing on the provision of inputs for a “screen-
and-treat” approach, availability of medical resources, and continuous monitoring programs
in risk zones.
KEY WORDS: Cryptococcosis; fungal pathogen; HIV/AIDS
1Centro de Estudios en Ecolog´
ıa Espacial y Medio Ambiente,
Ecogeograf´
ıa, Santiago, Chile.
2Departamento de Gesti ´
on Agraria, Facultad Tecnol ´
ogica, Uni-
versidad de Santiago de Chile, Santiago, Chile.
3Dipartimento Scienze Biomediche per la Salute, Universit`
a degli
Studi di Milano, Milano, Italy.
∗Address correspondence to Alberto J. Alaniz, Centro de Es-
tudios en Ecolog´
ıa Espacial y Medio Ambiente, Ecogeograf´
ıa,
Miguel Claro #2550, ˜
Nu ˜
noa, Santiago, Chile; alberto.alaniz@ug.
uchile.cl.
[Correction added on October 11, 2019 after first online publica-
tion: Inverted the legends of figure 3 and 4]
1. INTRODUCTION
Cryptococcus neoformans and Cryptococcus
gattii are two species complexes including the main
fungal pathogens that are etiological agents of
cryptococcosis (Hagen et al., 2015; Heitman, Kozel,
Kwon-Chung, Perfect, & Casadevall, 2011). These
infectious agents are present in the environment,
where they are able to survive in soil, plant materials,
and bird excreta, and to reproduce both sexually and
asexually through basidiospores and blastospores,
10272-4332/19/0100-0001$22.00/1 C2019 Society for Risk Analysis
2Alaniz et al.
which can disseminate in the environment and
colonize new sites as well as infect humans and
animals (May, Stone, Wiesner, Bicanic, & Nielsen,
2016; Velagapudi, Hsueh, Geunes-Boyer, Wright,
& Heitman, 2009). The most vulnerable hosts
are immunocompromised patients such as those
affected by AIDS, hematological malignancies,
autoimmune diseases; organ transplanted patients;
and patients under long-term therapy with corticos-
teroids (Williamson et al., 2016; Forsythe, Vogan, &
Xu, 2016). Human hosts are infected by inhalation
of spores that are then able to invade pulmonary
alveoli, causing pulmonary diseases or to dissemi-
nate through the bloodstream, often leading to fatal
meningitis (Forsythe et al., 2016; May et al., 2016;
Williamson et al., 2016).
Both C. neoformans and C. gattii species com-
plexes are present in Europe. The two species of
the C. neoformans species complex, C. neoformans
(AFLP1/VNI) and Cryptococcus deneoformans
(AFLP2/VNIV), together with their interspecies
hybrids, represent 59.1%, 18.3%, and 18.3% of
cases of cryptococcosis, respectively, whereas the C.
gattii species complex is only 4.1% being present in
Europe, the C. gattii s.s. (AFLP4/VGI) and Cryp-
tococcus tetragattii (AFLP7/VGIV) (Cogliati, 2013;
Viviani et al., 2006). Although cryptococcosis is
primarily associated with the C. neoformans species
complex in Europe, the role of the C. gattii species
complex could be even more significant in view of the
outbreak of cryptococcosis in the north Pacific coast
of Canada and the United States, affecting healthy
individuals and generating serious concern among
scientific, clinical, and public health institutions in the
last years (Harris, Lockhart, & Chiller, 2012; Kidd
et al., 2004). Recent environmental surveys carried
out in Europe and in the Mediterranean area showed
the presence of both Cryptococcus species complexes
in several tree species, confirming the importance of
trees as a natural niche and reservoir of these fungal
pathogens (Chowdhary et al., 2012; Cogliati et al.,
2016; Colom et al., 2012; Hagen et al., 2012; Linares
et al., 2015; Mahmoud, 1999; Mseddi et al., 2011). As
the survival rate of these agents in the environment
depends highly on environmental conditions, previ-
ous studies have estimated their fundamental niche
requirement, allowing the determination of a distri-
bution range in the continent (Cogliati et al., 2017).
These models have proven to be effective for the
identification of climatic dependence of the fungi,
however, different ecological studies have proposed
that other site conditions could influence their suc-
cess and survival in the environment (Granados &
Casta ˜
neda, 2006; Uejio, Mak, Manangan, Luber, &
Bartlett, 2015). These findings make it necessary to
evaluate and monitor continuously the changes in the
distribution of the species when more environmental
variables are added at finer grain scales. The risk of
exposure also depends on the presence of human
population that could be vulnerable to infection by
the spores of these agents (Rajasingham et al., 2017).
Given the increasing trend in HIV/AIDS cases
around the world, there is an urgent need to develop
new analyses and tools that allow improvement of
public health planning and resource management,
focusing on high risk geographical zones and future
contingencies (Naghavi et al., 2017).
Previous studies have combined ecological niche
modeling methodologies with human population
data to estimate the exposure risk to other infectious
agents (Alaniz, Bacigalupo, & Cattan, 2017; Alaniz,
Carvajal, Bacigalupo, & Cattan, 2019). The products
of these analyses consist of a series of spatially
explicit estimations of risk zones and potentially
affected people per pixel. The aims of the present
study are: (i) to estimate the potential geographic
distribution of the C. neoformans,C. deneoformans,
and C. gattii species complex in Europe and the
Mediterranean area; (ii) to determine the zones of
potential spatial interaction (PSI) or co-occurrence
of fungi; (iii) to identify the exposure risk zones
considering levels of risk; and (iv) to quantify the
population by risk level and country, including
HIV-infected patients.
2. METHODS
2.1. Potential Geographic Distribution of Fungal
Pathogens
A species distribution modeling (SDM) ap-
proach based on maximum entropy technique was
applied to estimate the potential geographic dis-
tribution of C. neoformans,C. deneoformans, and
the C. gattii species complex in Europe and the
Mediterranean area including Algeria, Egypt, Israel,
Lebanon, Libya, Morocco, Palestine, Syria, Tunisia,
and Turkey. The analysis was performed with Max-
Ent software v3.3.3k (Princeton University, USA,
https://www.cs.princeton.edu/˜schapire/maxent), wh-
ich is able to produce distribution maps starting from
two main sources of data, a species occurrence data
set and a set of environmental predictor variables
(Phillips, Anderson, Dud´
Ik, Schapire, & Blair, 2017).
Cryptococcus Risk in Europe 3
The occurrence data set of the present study included
the same data previously reported by Cogliati et al.
(2017) as well as those reported in GBIF (2018),
reaching 31 occurrence points for C. deneoformans,
84 for C. neoformans, and 21 for the C. gattii species
complex. This is the most complete database for
these fungal pathogens in Europe, however, due to
the scarcity of data for specific C. gattii species we
modeled the complete species complex that in Eu-
rope includes C. gattii s.s. and C. tetragattii.
Environmental variables included temperature
and precipitation data as well as 19 bioclimatic vari-
ables from the WorldClim2 Project, plus wind speed,
solar radiation, and vapor pressure (Fick & Hij-
mans, 2017); physiographic variables that included
elevation and topographic diversity (Theobald,
Harrison-Atlas, Monahan, & Albano,); and habitat
variables that included vegetation continuous field
(canopy closure), evenness vegetation index (EVI)
(vegetation greenness), net primary productivity
(plant growth in a year), and a normalized difference
water index (NDWI) (vegetation humidity). All the
habitat variables were generated using the mean
values of all the available images from MODIS
products between 2000 and 2017 (available as down-
loadable data) in the Google Earth Engine Platform
(Gorelick et al., 2017). The pixel scale of all the
environmental variables was 5 km2.
An exploratory model was first developed in
which all the environmental variables were included
with a threefold cross-validation technique for
each of the three fungi. This model estimates the
percentage contribution (PC) and the permutation
importance (PI) of the environmental variables. To
reduce potential multicollinearity and overfitting,
the variables with more than 1% of PC or PI were
selected. Since the selection of variables was per-
formed for each fungus, the resulting environmental
database was different for each. Finally, a model
was generated using only the selected environmental
variables, considering threefold cross-validation
for C. neoformans and C. deneoformans and the
5-bootstrap models technique for the C. gatti species
complex due to the lower occurrence number. Model
accuracy was evaluated by assessing the area under
the curve (AUC) of the receiver operator charac-
teristic (ROC) (Elith et al., 2011). A distribution
map for each fungal pathogen was generated and the
contribution of each variable was evaluated, as well
as the behavior of the response curves in relation to
the predicted suitability.
2.2. PSI Zones Between Fungal Pathogens
The protocol of Alaniz et al. (2019) and Alaniz,
Grez, and Zaviezo (2018) was followed to estimate
the potential interaction zones between the different
fungal pathogens. The continuous raster of suitability
was reclassified to convert it into a raster containing
binary values (present =1; absent =0), considering
the 10th percentile of the SDM suitability values as
a threshold for statistically significant probabilities.
Finally, the SDMs of the three fungal pathogens
were overlapped and the area of PSI as well as the
interacting organisms was identified and quantified.
2.3. Zones of Exposure Risk
The method described by Alaniz et al. (2017,
2018) was applied to estimate exposure risk consid-
ering two main factors: potential abundance of the
pathogenic agent and human population density.
The potential abundance of the fungus was estimated
through the reclassification of the continuous SDM
of each fungal pathogen into four intervals of suit-
ability, assigning them a numerical value correspond-
ing to each category: null =0, low =1, medium =
2, high =3. The null value was used for suitability
values under the 10th percentile, which were consid-
ered nonsignificant. The other three intervals were
classified using equal intervals. Suitability may be
considered a good proxy for potential abundance
of an organism, determined by the relationship
between suitability and fitness, which means that in
the zones where there is high suitability the organism
is expected to have a high reproductive potential and
a low mortality rate (Ehrl´
en & Morris, 2015; Hirzel
& Le Lay, 2008). The human population density
was determined using the estimation of SEDAC-
NASA for the year 2020 from the project Gridded
Population of the World (GPW) v4 (CIESIN &
Columbia University, 2017a). This estimation is
based on local censuses and government population
data and projections. The raster of population
density was reclassified in four levels; null =0(0–
1 inhabitants/km2), low =1(>1–10 inhabitants/km2),
medium =2(>10–100 inhabitants/km2), and high =
3(>100 inhabitants/km2). Then the new discrete
rasters were multiplied using the raster calculator
tool in a geographic information system software
(QGIS) This multiplication was made for each of the
three fungal pathogens, obtaining a map with nine
risk levels ranging from null to very high (Fig. 1).
4Alaniz et al.
Cryptococcus
0123
n
oita
lupoPyti
sne
d
00000
10123
20246
30369
Result of grid
multiplication Exposure risk
0 Null
1Verylow
2 Low
3Medium
4 Medium
6High
9 Very high
Fig. 1. Double entry matrix used in the raster calculator to gen-
erate the risk map for each fungal pathogen (left panel) and re-
sults of the raster multiplication with the corresponding level of
risk (right panel).
2.4. Quantification of the People Under Risk
The number of people per risk level, country,
and fungal pathogen were calculated by overlapping
the potential exposure risk map with a map of
population count per pixel for the year 2020 from the
GPW v4 (CIESIN & Columbia University, 2017b).
The number of HIV-infected patients under risk of
cryptococcosis was also estimated by multiplying the
HIV prevalence, according to 2017 data released by
World Health Organization (WHO, 2018), by the
estimated people under exposure risk per country.
The analysis considered only people potentially
exposed to the medium, high, and very high levels,
in order to sharpen the prediction and to generate a
more conservative estimation.
3. RESULTS
3.1. Potential Geographic Distribution
The models reached AUC of 0.986 ±0.006,
0.961 ±0.014, and 0.935 ±0.024 for the C. gattii
species complex, C. neoformans, and C. dene-
oformans, respectively. In the C. gattii species
complex model, the most significant variables were
water vapor pressure, mean temperature of the
coldest quarter, and precipitation of the driest
month, with PC of 43.8%, 11.2%, and 10.9%,
respectively. The suitability increased with in-
creasing vapor pressure, mean temperature of the
coldest quarter, and precipitation of the driest
month, reaching a peak at 1.5 kPa, 10 °C, and
10 mm, respectively. In the model generated for C.
neoformans, the variables with the most significant
PC were water vapor pressure, minimum temper-
ature of the coldest month, and canopy closure,
with 49.2%, 17.2%, and 8%, respectively. A peak of
suitability was observed at 1.4 kPa for water vapor
pressure, at 6 °C for temperature of coldest month
and at 7% for canopy closure. In the model for
C. deneoformans, the NDWI, precipitation of the
driest month, and EVI showed the highest PC, with
14.4%, 13.2%, and 11.6%, respectively. The peaks
of suitability were observed at 0 for NDWI, 10 mm
for precipitation of driest month, and 0.25 for EVI.
The distribution of the C. gattii species complex is
mainly concentrated in the Mediterranean coastal
zones of Europe, also showing high suitability in the
Nile River delta; central coastal zone of Portugal;
northern Tunisia; and the islands of Sardinia, Cor-
sica, and Palma de Mallorca. C. neoformans occurs
in similar zones to the C. gattii species complex, but
with a wider distribution in zones such as southwest
France; the Atlantic coast of Portugal; and the coasts
of Algeria, Turkey, Syria, and Lebanon. Finally, C.
deneoformans is distributed across all continents,
with highest suitability in the Mediterranean coast
of Europe, Northern France, Portugal, the southern
United Kingdom, northern Italy, and Turkey (Fig. 2).
3.2. Zones of PSI
The total area of distribution of all three fungi
in Europe was 2,685,050 km2, of which 51.7% is
suitable for C. deneoformans survival followed by
C. neoformans and the C. gattii species complex,
covering 12.5% and 2.3% of the area, respectively.
The areas of co-occurrence between the C. gat-
tii species complex and C. neoformans include
75,401 km2(2.8% of the total), being important in
Malta, Portugal, and Tunisia with 14.5%, 6.7%, and
4.4% of their respective country areas. The zones
of co-occurrence of the C. gattii species complex
and C. deneoformans are much more restricted, with
18,524 km2(0.7% of the total), being present mainly
in Spain, Greece, and Tunisia where they represent
1.9%, 0.6%, and 0.3% of the country area, respec-
tively. The zones of potential co-occurrence between
C. neoformans and C. deneoformans are distributed
in 539,340 km2(20.1% of the total), being important
in Portugal, Italy, Albania, and France, with 27.6%,
18.6%, 17.9%, and 17% of their respective country
areas. Finally, the zones of co-occurrence of C.
neoformans,C. deneoformans, and C. gattii species
complex covers 268,340 km2(10% of the total), being
highly important in countries such as Italy, Greece,
and Portugal, with 21.1%, 12.8%, and 12.5% of
their respective country areas. Considering all three
fungal pathogens and co-occurrence zones, 13 coun-
tries have more than 30% of their total areas with
Cryptococcus Risk in Europe 5
Fig. 2. Map of suitability for the Cryptococcus gattii species com-
plex (A), C. neoformans (B), and C. deneoformans (C). Color
scale from yellow to red covers low to high suitability, respectively.
Black color represents non-significant suitability.
potential presence of these pathogens (Figs. 3 and 4;
Tables A1 and A2 in the Supporting Information).
3.3. Exposure Risk Zones
The risk associated with the C. gattii species
complex exposure is mainly distributed along the
Mediterranean coast of Spain, the Balearic Islands,
Italy, Tunisia, and Greece. This pathogen showed
also a high risk in the central Atlantic coast of Portu-
gal and the Nile River delta in Egypt. The risk zones
of C. neoformans have a similar geographic range to
the C. gattii species complex but are extended over
a wider area, showing risk also along the Atlantic
coast and the central-northern area of France, the
Fig. 3. Map of potential exposure risk associated with the Crypto-
coccus gattii species complex (A), C. neoformans (B), and C. dene-
oformans (C).
Mediterranean coast of Algeria, Lebanon, Morocco,
Syria, and Turkey. C. deneoformans has the widest
area of risk in Europe, showing risk in the same zones
as the other two species, but being present also in
Albania, Austria, Czech Republic, France, Germany,
Switzerland, and the United Kingdom (Figs. 3 and 4).
3.4. Quantification of People Under Risk and
HIV-Infected Patients
There are 137.65 million people exposed to risk
due to C. gattii species complex in Europe, while
those exposed to C. neoformans and C. deneofor-
mans are 266.58 and 360.78 million, respectively
(Table I). The population exposed to C. gattii species
6Alaniz et al.
Fig. 4. Map of co-occurrence for the Cryptococcus gattii species complex (A), C. neoformans (B), and C. deneoformans (C).
Table I. Population Exposed to Cryptococcus neoformans,C. deneoformans,andtheC. gattii Species Complex (Including C. gattii s.s. and
C. tetragattii) Stratified by Risk Level
Number of People Under Risk (Millions)
Fungal Pathogen Very Low Low Medium High Very High Total
HIV Patients
(Individuals)
C. gattii species complex 0.44 3.64 70.36 43.81 19.41 137.65 271,895
C. neoformans 0.59 14.56 129.72 71.21 50.48 266.58 624,839
C. deneoformans 1.03 23.64 155.00 97.52 83.58 360.78 823,148
complex represents more than 30% of the total
in only six countries; the most affected are Egypt,
Spain, and Portugal with 55.2%, 49.4%, and 45.9%,
respectively (Table A3 in the Supporting Informa-
tion). For C. neoformans, 16 countries have more
than 30% of their population exposed; Lebanon, San
Marino, and Portugal are the most affected, with
99.4%, 98.5%, and 96.7%, respectively (Table A4
in the Supporting Information). In 19 countries, the
population exposed to C. deneoformans represents
more than 30% of the total population; San Marino,
Italy, and the United Kingdom are the most affected
with 99.4%, 96.5%, and 95.7%, respectively (Table
A5 in the Supporting Information).
There are 271,895 HIV-infected patients po-
tentially exposed to the C. gattii species complex,
while for C. neoformans and C. deneoformans there
are 624,839 and 823,148, respectively. The countries
with the highest number of HIV-infected people
potentially exposed to C. gattii species complex were
Spain, Egypt, and Italy, with 86,210, 57,159, and
35,901 individuals, respectively. The exposure risk
to C. neoformans mainly affects France, Spain, and
Italy, with 208,963, 109,068, and 82,543, respectively.
The highest risk for HIV-infected patients exposed
to C. deneoformans was observed in France with
239,809 people, the United Kingdom with 123,462
people, and Italy with 106,522 people (Table A6 in
the Supporting Information).
4. DISCUSSION
The SDM methodology consists of the predic-
tive modeling of the geographical distribution of
organisms based on their known environmental
requirements, including geographical occurrences
of the species and a set of biotic/abiotic predictor
variables (Elith et al., 2011; Phillips et al., 2017). This
methodology has proven to be useful to quantify the
potential risk associated with infectious agents, by
combining pathogen suitability maps with spatially
explicit human population estimates (Alaniz et al.,
2017, 2019). The biological cycle of C. neoformans
and C. gattii species complexes is highly influenced
Cryptococcus Risk in Europe 7
by: (i) vegetation, due to the dependence on the
available resources related to this type of habitat
(e.g., organic plant debris; soil microorganisms as
intermediate hosts; and higher vertebrate hosts
such as reptiles, mammals, and birds) (DeBess,
Lockhart, Iqbal, & Cieslak, 2014; May et al., 2016);
(ii) wind, associated with the dissemination of spores
(Velagapudi et al., 2009); (iii) solar radiation, which
promotes the airborne concentration of these types
of fungi (Granados & Casta ˜
neda, 2006; Uejio et al.,
2015); and (iv) water vapor pressure because hu-
midity increases the viability of spores (Granados &
Casta ˜
neda, 2006; Uejio et al., 2015). Consequently,
aiming to sharpen the suitability prediction, envi-
ronmental variables accounting for each of these
specific biological requirements were included in the
SDM. Previous studies that estimated the potential
distribution of these fungal pathogens (Cogliati
et al., 2017) generated a baseline with the specific
environmental requirements of the species, being
a cornerstone for the development of the present
study. The species distribution models generated
in the present study are concordant with those
reported by Cogliati et al. (2017), presenting similar
spatial distribution patterns for all three fungal
pathogens. However, the inclusion of new variables
has produced a more accurate model prediction, by
expanding the suitable areas and increasing the spa-
tial definition and resolution of the maps. Although
this analysis was performed for the C. gattii species
complex, C. neoformans and C. deneoformans,itwas
not possible for C. neoformans interspecies hybrids,
which represent 18% of the cases of cryptococcosis
occurring in Europe (Cogliati et al., 2016) because
of the scarce number of available environmental
strains. This could be associated with the scarce
presence of hybrids in environment, being gener-
ally reported in human patients during infection
(Tomazin, Matos, Meis, & Hagen, 2018). However,
the co-occurrence maps produced during the analy-
sis could be useful to address future environmental
surveys in the areas where the distribution of the two
species overlaps, and therefore to clarify the origin of
these strains and their impact on the epidemiology of
cryptococcosis.
Exposure risk was evaluated using a framework
that integrates two factors, threat and vulnerability
(Alaniz et al., 2017, 2019). The potential abundance
of the infectious agent predicted by the SDM was
considered as proportional to the threat, whereas
vulnerability was considered as proportional to the
density of potential human hosts, reported in a map
of population density. However, vulnerability of
human hosts for cryptococcosis is not homogeneous
in the population; immunocompromised patients
(e.g., HIV/AIDS, organ transplants, hematological
malignancies, long-term corticosteroid therapy,
chemotherapy, autoimmune diseases) are the most
affected (Limper, Adenis, Le, & Harrison, 2017;
Rajasingham et al., 2017; Singh, Dromer, Perfect, &
Lortholary, 2008). It was not possible to determine
the geographical distribution of these patients, due
to the unavailability of a geo-located information
database of the cases. This risk framework has been
previously used to model the risk associated with
vector-borne infectious diseases (Alaniz et al., 2017,
2019), proving to be useful to generate risk maps at
wide geographical scales (continental and global).
However, the generation of finer grain national and
subnational risk maps is recommended.
Our results identified that the number of people
under risk of exposure to C. neoformans,C. deneo-
formans, and the C. gattii species complex exceeds
100 million people for each fungal pathogen, show-
ing risk in almost all countries in Europe and the
Mediterranean area. These fungal pathogens have
been considered highly dangerous for the population,
due to the difficulty in controlling the spores in the
environment and the high mortality rate in specific
types of patients (Bielska & May, 2015; Rajasingham
et al., 2017). However, it is necessary to highlight
that the epidemiology of these fungal pathogens
is very dissimilar. Although in our model the C.
gattii species complex presents the most restricted
geographic distribution and the lowest number of
people exposed (for C. gattii s.s. and C. tetragattii),
some epidemiological characteristics could be of
concern: (i) its capacity to infect immunocompetent
patients (Bielska & May, 2015); (ii) the recent in-
crease in the number of epidemic outbreaks outside
the source area of some species (e.g., C. deuterogattii
AFLP6/VGII from tropical and subtropical zones
in America), which makes it an emerging agent that
could become a serious threat in the future due to
climate change (Bielska & May, 2015; Hagen et al.,
2012; Harris et al., 2012; Kidd et al., 2004); (iii) as it
is a new emergent agent, little research information
is available and this could make its management
difficult (e.g., predictability of outbreaks, effectivity
of treatments, virulence mechanism) (Bielska &
May, 2015). Thus, the presence of 63.21 million
people exposed to high and very high level of risk
due to the C. gattii species complex generates seri-
ous concerns especially for the apparently healthy
8Alaniz et al.
subjects; spatial prediction could represent an essen-
tial tool for the implementation of preventive public
health and contingency plans as well as focusing the
distribution of screening tests and diagnosis methods
in the potentially affected population (Harris et al.,
2013). It is important to understand that our model
could underestimate the distribution of some C.
gattii species complex such as C. gattii s.s., which
has only one report in the Netherlands (Chowdhary
et al., 2012). It is necessary to maintain the moni-
toring efforts, aiming to detect more environmental
occurrences of this species in this zone.
The number of people exposed to the C. neofor-
mans species at high and very high levels of risk was
121.70 and 181.11 million people for C. neoformans
and C. deneoformans¸respectively. The latter has
a higher tolerance to lower temperatures, allowing
to reach higher latitudes, increasing the amount of
people under risk. The environmental niche of C. de-
neoformans is wider than C. neoformans and C. gattii
species complex, the last two associated with tropical
and subtropical climatic conditions influenced by the
thermal stability provided by ocean (Cogliati et al.,
2017).
The results of this study also show that more
than a half million people with HIV are exposed
to C. neoformans, which is one of the important
causes of mortality for HIV/AIDS patients. In
Europe, the medical knowledge and practices about
this pathogen are high (Rajasingham et al., 2017),
therefore future efforts should be focused to enable
an earlier and cost-effective “screen-and-treat” re-
sponse (Mfinanga et al., 2015). The risk maps and the
statistics generated here represent a useful tool to
provide more effective government health planning
of preventive medicine of HIV/AIDS. This allows
governments to focus, and ensures: (i) the provision
of inputs for a “screen-and-treat” approach; (ii) the
availability of medical resources (e.g., drugs, tests,
professionals); and (iii) a continuous monitoring pro-
gram in high risk zones (Mfinanga et al., 2015; Molloy
et al., 2018). The present methodological approach
could be used to estimate the risk associated with
Cryptococcus in other zones of the world, where the
prevalence of HIV is higher and the income, health
conditions, and medical resources are limited such
as Africa, Asia, and Latin America (Naghavi et al.,
2017; WHO, 2018). The results, maps, and statistical
output reported here represent useful tools for
reduction of mortality rates due to cryptococcosis.
Finally, we encourage the governments and public
health agencies to generate more epidemiological
studies aiming to cover all the European countries,
especially using clinical characteristics and molecular
characterization of the involved cryptococcal strains.
ACKNOWLEDGMENTS
We acknowledge N.G.O Ecogeograf´
ıa for the
support. Pablo M. Vergara thank FONDECYT (No.
1180978) for providing funds.
REFERENCES
Alaniz, A. J., Bacigalupo, A., & Cattan, P. E. (2017). Spatial quan-
tification of the world population potentially exposed to Zika
virus. International Journal of Epidemiology,46(3), 966–975.
https://doi.org/10.1093/ije/dyw366
Alaniz, A. J., Carvajal, M. A., Bacigalupo, A., & Cattan,
P. E. (2019). Global spatial assessment of Aedes aegypti
and Culex quinquefasciatus: A scenario of Zika virus expo-
sure. Epidemiology and Infection,147, e52. https://doi.org/
10.1017/S0950268818003102
Alaniz, A. J., Grez, A. A., & Zaviezo, T. (2018). Potential spatial
interaction of the invasive species Harmonia axyridis (Pallas)
with native and endemic coccinellids. Journal of Applied Ento-
mology,142(5), 513–524. https://doi.org/10.1111/jen.12498
Bielska, E., & May, R. C. (2015). What makes Cryptococ-
cus gattii a pathogen? FEMS Yeast Research,16(1), 1–12.
https://doi.org/10.1093/femsyr/fov106
Chowdhary, A., Randhawa, H. S., Boekhout, T., Hagen, F.,
Klaassen, C. H., & Meis, J. F. (2012). Temperate climate
niche for Cryptococcus gattii in Northern Europe. Emerg-
ing Infectious Diseases,18(1), 172–174. https://doi.org/10.3201/
eid1801.111190
CIESIN and Columbia University. (2017a). Gridded population of
the world, version 4 (GPWv4): Population density, revision 10.
Palisades, NY: NASA Socioeconomic Data and Applications
Center (SEDAC). https://doi.org/10.7927/H4DZ068D
CIESIN and Columbia University. (2017b). Gridded population
of the world, version 4 (GPWv4): Population count, revision 10.
Palisades, NY: NASA Socioeconomic Data and Applications
Center (SEDAC). https://doi.org/10.7927/H4PG1PPM
Cogliati, M. (2013). Global molecular epidemiology of Cryptococ-
cus neoformans and Cryptococcus gattii: An atlas of the molec-
ular types. Scientifica,2013. https://doi.org/10.1155/2013/675213
Cogliati, M., D’Amicis, R., Zani, A., Montagna, M. T., Caggiano,
G., De Giglio, O., .. . Colom, M. F. (2016). Environmental
distribution of Cryptococcus neoformans and C. Gattii around
the Mediterranean basin. FEMS Yeast Research,16(4), 1–12.
https://doi.org/10.1093/femsyr/fow045
Cogliati, M., Puccianti, E., Montagna, M. T., De Donno, A., Su-
sever, S., Ergin, C., . .. Colom, M. F. (2017). Fundamental niche
prediction of the pathogenic yeasts Cryptococcus neoformans
and Cryptococcus gattii in Europe. Environmental Microbiol-
ogy,19(10), 4318–4325. https://doi.org/10.1111/1462-2920.13915
Colom, M. F., Hagen, F., Gonzalez, A., Mellado, A., Morera, N.,
Linares, C., . . . S ´
anchez, M. (2012). Ceratonia siliqua (carob)
trees as natural habitat and source of infection by Cryptococ-
cus gattii in the Mediterranean environment. Medical Mycol-
ogy,50(1), 67–73. https://doi.org/10.3109/13693786.2011.574239
DeBess, E., Lockhart, S. R., Iqbal, N., & Cieslak, P. R.
(2014). Isolation of Cryptococcus gattii from Oregon soil
and tree bark, 2010–2011. BMC Microbiology,14(1), 2–5.
https://doi.org/10.1186/s12866-014-0323-2
Ehrl´
en, J., & Morris, W. F. (2015). Predicting changes in the
distribution and abundance of species under environmental
Cryptococcus Risk in Europe 9
change. Ecology Letters,18(3), 303–314. https://doi.org/
10.1111/ele.12410
Elith, J., Phillips, S. J., Hastie, T., Dud´
ık, M., Chee, Y. E.,
& Yates, C. J. (2011). A statistical explanation of Max-
Ent for ecologists. Diversity and Distributions,17(1), 43–57.
https://doi.org/10.1111/j.1472-4642.2010.00725.x
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-
km spatial resolution climate surfaces for global land ar-
eas. International Journal of Climatology,37(12), 4302–4315.
https://doi.org/10.1002/joc.5086
Forsythe, A., Vogan, A., & Xu, J. (2016). Genetic and environ-
mental influences on the germination of basidiospores in the
Cryptococcus neoformans species complex. Scientific Reports,
6(1), 1–12. https://doi.org/10.1038/srep33828
GBIF. (2018). GBIF occurrence, download. https://doi.org/
10.15468/dl.ofjxcm and https://doi.org/10.15468/dl.gmwftf
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau,
D., & Moore, R. (2017). Google earth engine: Planetary-scale
geospatial analysis for everyone. Remote Sensing of Environ-
ment,202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Granados, D. P., & Casta ˜
neda, E. (2006). Influence of cli-
matic conditions on the isolation of members of the Cryp-
tococcus neoformans species complex from trees in Colom-
bia from 1992–2004. FEMS Yeast Research,6(4), 636–644.
https://doi.org/10.1111/j.1567-1364.2006.00090.x
Hagen, F., Colom, M. F., Swinne, D., Tintelnot, K., Iatta, R., Mon-
tagna, M. T., . .. Klaassen, C. H. W. (2012). Autochthonous and
dormant Cryptococcus gattii infections in Europe. Emerging In-
fectious Diseases,18(10), 1618–1624.
Hagen, F., Khayhan, K., Theelen, B., Kolecka, A., Polacheck,
I., Sionov, E., . . . Boekhout, T. (2015). Recognition of seven
species in the Cryptococcus gattii/Cryptococcus neoformans
species complex. Fungal Genetics and Biology,78, 16–48.
https://doi.org/https://doi.org/10.1016/j.fgb.2015.02.009
Harris, J., Lockhart, S., & Chiller, T. (2012). Cryptococcus gattii:
Where do we go from here? Medical Mycology,50(2), 113–129.
https://doi.org/10.3109/13693786.2011.607854
Harris, J. R., Lockhart, S. R., Sondermeyer, G., Vugia, D. J., Crist,
M. B., D’Angelo, M. T., .. . Park, B. J. (2013). Cryptococ-
cus gattii infections in multiple states outside the US Pacific
Northwest. Emerging Infectious Diseases,19(10), 1620–1626.
https://doi.org/10.3201/eid1910.130441
Heitman, J., Kozel, T. R., Kwon-Chung, K. J., Perfect, J. R., &
Casadevall, A. (2011). Cryptococcus: From human pathogen to
model yeast. Washington, DC: ASM Press.
Hirzel, A. H., & Le Lay, G. (2008). Habitat suitability modelling
and niche theory. Journal of Applied Ecology,45(5), 1372–1381.
https://doi.org/10.1111/j.1365-2664.2008.01524.x
Kidd, S. E., Hagen, F., Tscharke, R. L., Huynh, M., Bartlett,
K. H., Fyfe, M., . .. Meyer, W. (2004). A rare genotype
of Cryptococcus gattii caused the cryptococcosis outbreak on
Vancouver Island (British Columbia, Canada). Proceedings
of the National Academy of Sciences,101(49), 17258–17263.
https://doi.org/10.1073/pnas.0402981101
Limper, A. H., Adenis, A., Le, T., & Harrison, T. S. (2017).
Fungal infections in HIV/AIDS. The Lancet Infectious
Diseases,17(11), e334–e343. https://doi.org/10.1016/S1473-
3099(17)30303-1
Linares, C., Colom, M. F., Torreblanca, M., Esteban, V., Romera,
´
A., & Hagen, F. (2015). Environmental sampling of Cera-
tonia siliqua (carob) trees in Spain reveals the presence of
the rare Cryptococcus gattii genotype AFLP7/VGIV. Revista
Iberoamericana de Micolog´
ıa,32(4), 269–272. https://doi.org/
https://doi.org/10.1016/j.riam.2014.11.002
Mahmoud, Y. A. (1999). First environmental isolation of Cryp-
tococcus neoformans var. neoformans and var. gatti from
the Gharbia Governorate, Egypt. Mycopathologia,148(2),
83–86.
May, R. C., Stone, N. R. H., Wiesner, D. L., Bicanic, T., & Nielsen,
K. (2016). Cryptococcus: From environmental saprophyte to
global pathogen. Nature Reviews Microbiology,14(2), 106–117.
https://doi.org/10.1038/nrmicro.2015.6
Mfinanga, S., Chanda, D., Kivuyo, S. L., Guinness, L., Bot-
tomley, C., Simms, V., . . . Jaffar, S. (2015). Cryptococcal
meningitis screening and community-based early adherence
support in people with advanced HIV infection starting an-
tiretroviral therapy in Tanzania and Zambia: An open-label,
randomised controlled trial. Lancet,385(9983), 2173–2182.
https://doi.org/10.1016/S0140-6736(15)60164-7
Molloy, S. F., Kanyama, C., Heyderman, R. S., Loyse, A., Kouan-
fack, C., Chanda, D., . . . Harrison, T. S. (2018). Antifun-
gal combinations for treatment of cryptococcal meningitis in
Africa. New England Journal of Medicine,378(11), 1004–1017.
https://doi.org/10.1056/NEJMoa1710922
Mseddi, F., Sellami, A., Jarboui, M. A., Sellami, H., Makni, F., &
Ayadi, A. (2011). First environmental isolations of Cryptococ-
cus neoformans and Cryptococcus gattii in Tunisia and review
of published studies on environmental isolations in Africa. My-
copathologia,171(5), 355–360.
Naghavi, M., Abajobir, A. A., Abbafati, C., Abbas, K. M., Abd-
Allah, F., Abera, S. F., . .. Murray, C. J. L. (2017). Global, re-
gional, and national age-sex specific mortality for 264 causes of
death, 1980–2016: A systematic analysis for the Global Bur-
den of Disease Study 2016. Lancet,390(10100), 1151–1210.
https://doi.org/10.1016/S0140-6736(17)32152-9
Phillips, S. J., Anderson, R. P., Dud´
Ik, M., Schapire, R.
E., & Blair, M. E. (2017). Opening the black box: An
open-source release of Maxent. Ecography,40(7), 887–893.
https://doi.org/10.1111/ecog.03049
Rajasingham, R., Smith, R. M., Park, B. J., Jarvis, J. N., Govender,
N. P., Chiller, T. M., . .. Boulware, D. R. (2017). Global burden
of disease of HIV-associated cryptococcal meningitis: An up-
dated analysis. The Lancet Infectious Diseases,17(8), 873–881.
https://doi.org/10.1016/S1473-3099(17)30243-8
Singh, N., Dromer, F., Perfect, J. R., & Lortholary, O. (2008).
Cryptococcosis in solid organ transplant recipients: Current
state of the science. Clinical Infectious Diseases,47(10), 1321–
1327. https://doi.org/10.1086/592690
Theobald, D. M., Harrison-Atlas, D., Monahan, W. B., & Al-
bano, C. M. (2015). Ecologically-relevant maps of landforms
and physiographic diversity for climate adaptation planning.
Plos One,10(12), e0143619.
Tomazin, R., Matos, T., Meis, J. F., & Hagen, F. (2018). Molec-
ular characterization and antifungal susceptibility testing of
sequentially obtained clinical Cryptococcus deneoformans and
Cryptococcus neoformans isolates from Ljubljana, Slovenia.
Mycopathologia,183(2), 371–380. https://doi.org/10.1007/
s11046-017-0214-9
Uejio, C. K., Mak, S., Manangan, A., Luber, G., & Bartlett, K.
H. (2015). Climatic influences on Cryptoccoccus gattii popu-
lations, Vancouver Island, Canada, 2002–2004. Emerging In-
fectious Diseases,21(11), 1989–1996. https://doi.org/10.3201/
eid2111.141161
Velagapudi, R., Hsueh, Y. P., Geunes-Boyer, S., Wright, J. R., &
Heitman, J. (2009). Spores as infectious propagules of Crypto-
coccus neoformans.Infection and Immunity,77(10), 4345–4355.
https://doi.org/10.1128/IAI.00542-09
Viviani, M. A., Cogliati, M., Esposto, M. C., Lemmer, K.,
Tintelnot, K., Valiente, M. F. C. . . . European Confedera-
tion of Medical Mycology (ECMM) Cryptococcosis Working
Group. (2006) Molecular analysis of 311 Cryptococcus neo-
formans isolates from a 30-month ECMM survey of cryp-
tococcosis in Europe. FEMS Yeast Research,6(4), 614–619.
https://doi.org/10.1111/j.1567-1364.2006.00081.x
World Health Organization (WHO). (2018). Prevalence of HIV
among adults aged 15 to 49 (%). Retrieved from https://apps.
10 Alaniz et al.
who.int/gho/athena/data/GHO/MDG_0000000029?filter=CO
UNTRY:*;REGION:*&format=xml&profile=excel
Williamson, P. R., Jarvis, J. N., Panackal, A. A., Fisher, M. C.,
Molloy, S. F., Loyse, A., & Harrison, T. S. (2016). Cryptococ-
cal meningitis: Epidemiology, immunology, diagnosis and ther-
apy. Nature Reviews Neurology,13(1), 13–24. https://doi.org/
10.1038/nrneurol.2016.167
SUPPORTING INFORMATION
Additional supporting information may be found on-
line in the Supporting Information section at the end
of the article.
Table A1. Area of Geographic Distribution and Co-
occurrence Expressed in km2of Cryptococcus gattii
Species Complex (A), C. neoformans (B),
C. deneoformans (C), C. gattii Species Complex and
C. neoformans (A +B), C. gattii and C. deneofor-
mans (A +C), C. neoformans and C. deneoformans
(B +C), and all the Fungal Pathogens (A +B+C)
Table A2. Relative Proportion of Area Under Risk
per Country Expressed in Percentages
Table A3. Population Potentially Exposed to Crypto-
coccus gattii Species Complex per Level of Risk and
Country
Table A4. Population Potentially Exposed to Cryp-
tococcus neoformans per Level of Risk and Country
Table A5. Population Potentially Exposed to Crypto-
coccus deneoformans per Level of Risk and Country
Table A6. HIV-Infected Patients Potentially Ex-
posed to Each Fungus per Country Considering the
Specific Prevalence
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