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*For correspondence: jane.
messina@zoo.ox.ac.uk (JPM);
sihay@uw.edu (SIH)
Competing interest: See
page 13
Funding: See page 13
Received: 15 February 2016
Accepted: 10 April 2016
Published: 19 April 2016
Reviewing editor: Mark Jit,
London School of Hygiene &
Tropical Medicine, and Public
Health England, United Kingdom
Copyright Messina et al. This
article is distributed under the
terms of the Creative Commons
Attribution License, which
permits unrestricted use and
redistribution provided that the
original author and source are
credited.
Mapping global environmental suitability
for Zika virus
Jane P Messina
1
*, Moritz UG Kraemer
1
, Oliver J Brady
2
, David M Pigott
2,3
,
Freya M Shearer
2
, Daniel J Weiss
1
, Nick Golding
4
, Corrine W Ruktanonchai
5
,
Peter W Gething
1
, Emily Cohn
6
, John S Brownstein
6
, Kamran Khan
7,8
,
Andrew J Tatem
5,9
, Thomas Jaenisch
10,11
, Christopher JL Murray
3
,
Fatima Marinho
12
, Thomas W Scott
13
, Simon I Hay
2,3
*
1
Department of Zoology, University of Oxford, Oxford, United Kingdom;
2
Wellcome
Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom;
3
Institute for Health Metrics and Evaluation, University of Washington, Seattle,
United States;
4
Department of BioSciences, University of Melbourne, Parkville,
United Kingdom;
5
WorldPop project, Department of Geography and Environment,
University of Southampton, Southampton, United Kingdom;
6
Boston Children’s
Hospital, Harvard Medical School, Boston, United Kingdom;
7
Department of
Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada;
8
Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Canada;
9
Flowminder Foundation, Stockholm, Sweden;
10
Section Clinical Tropical Medicine,
Department for Infectious Diseases, Heidelberg University Hospital, Heidelberg,
Germany;
11
German Centre for Infection Research (DZIF), Heidelberg partner site,
Heidelberg, Germany;
12
Secretariat of Health Surveillance, Ministry of Health Brazil,
Brasilia, Brazil;
13
Department of Entomology and Nematology, University of
California Davis, Davis, United States
Abstract Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes,
which also act as vectors for dengue and chikungunya viruses throughout much of the tropical
world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In
2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in
Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barre´ syndrome
observed in this outbreak have raised concerns about continued global spread of Zika virus,
prompting its declaration as a Public Health Emergency of International Concern by the World
Health Organization. We conducted species distribution modelling to map environmental suitability
for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable
environmental conditions with over 2.17 billion people inhabiting these areas.
DOI: 10.7554/eLife.15272.001
Introduction
Zika virus (ZIKV) is an emerging arbovirus carried by mosquitoes of the genus Aedes (Musso et al.,
2014). Although discovered in Uganda in 1947 (Dick et al., 1952;Dick, 1953) ZIKV was only known
to cause sporadic infections in humans in Africa and Asia until 2007 (Lanciotti et al., 2008), when it
caused a large outbreak of symptomatic cases on Yap island in the Federated States of Micronesia
(FSM), followed by another in French Polynesia in 2013–14 and subsequent spread across Oceania
(Musso et al., 2015a). In the 2007 Yap island outbreak, it was estimated that approximately 20% of
ZIKV cases were symptomatic. While indigenous transmission of ZIKV to humans was reported for
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 1 of 19
RESEARCH ARTICLE
the first time in Latin America in 2015 (Zanluca et al., 2015;World Health Organisation, 2015),
recent phylogeographic research estimates that the virus was introduced into the region between
May and December 2013 (Faria et al., 2016). This recent rapid spread has led to concern that the
virus is following a similar pattern of global expansion to that of dengue and chikungunya
(Musso et al., 2015a).
ZIKV has been isolated from 19 different Aedes species (Haddow et al., 2012;Grard et al.,
2014), but virus has been most frequently found in Ae. aegypti (Monlun et al., 1992;
Marchette et al., 1969;Smithburn, 1954;Pond, 1963;Faye et al., 2008;Foy et al., 2011b;
WHO Collaborating Center for Reference and Research on Arboviruses and Hemorrhagic Fever
Viruses: Annual Report, 1999). These studies were based upon ancestral African strains of ZIKV,
but the current rapid spread of ZIKV in Latin America is indicative of this highly efficient arbovirus
vector (Marcondes and Ximenes, 2015). The relatively recent global spread of Ae. albopictus
(Benedict et al., 2007;Kraemer et al., 2015c) and the rarity of ZIKV isolations from wild mosquitoes
may also partially explain the lower frequency of isolations from Ae. albopictus populations. Whilst
virus transmission by Ae. albopictus and other minor vector species has normally resulted in only a
small number of cases (Kutsuna et al., 2015;Roiz et al., 2015), these vectors do pose the threat of
limited transmission (Grard et al., 2014). The wide geographic distribution of Ae. albopictus com-
bined with the frequent virus introduction via viraemic travellers (McCarthy 2016;Bogoch et al.,
2016;Morrison et al., 2008;Scott and Takken, 2012), means the risk for ZIKV infection via this vec-
tor must therefore also be considered in ZIKV mapping.
The fact that ZIKV reporting was limited to a few small areas in Africa and Asia until 2007 means
that global risk mapping has not, until recently, been a priority (Pigott et al., 2015b). Recent associ-
ations with Guillain-Barre´ syndrome in adults and microcephaly in infants born to ZIKV-infected
mothers (World Health Organisation, 2015;Martines et al., 2016) have revealed that ZIKV could
lead to more severe complications than the mild rash and flu-like symptoms that characterize the
eLife digest Zika virus is transmitted between humans by mosquitoes. The majority of infections
cause mild flu-like symptoms, but neurological complications in adults and infants have been found
in recent outbreaks.
Although it was discovered in Uganda in 1947, Zika only caused sporadic infections in humans
until 2007, when it caused a large outbreak in the Federated States of Micronesia. The virus later
spread across Oceania, was first reported in Brazil in 2015 and has since rapidly spread across Latin
America. This has led many people to question how far it will continue to spread. There was
therefore a need to define the areas where the virus could be transmitted, including the human
populations that might be risk in these areas.
Messina et al. have now mapped the areas that provide conditions that are highly suitable for the
spread of the Zika virus. These areas occur in many tropical and sub-tropical regions around the
globe. The largest areas of risk in the Americas lie in Brazil, Colombia and Venezuela. Although Zika
has yet to be reported in the USA, a large portion of the southeast region from Texas through to
Florida is highly suitable for transmission. Much of sub-Saharan Africa (where several sporadic cases
have been reported since the 1950s) also presents an environment that is highly suitable for the Zika
virus. While no cases have yet been reported in India, a large portion of the subcontinent is also
suitable for Zika transmission.
Over 2 billion people live in Zika-suitable areas globally, and in the Americas alone, over 5.4
million births occurred in 2015 within such areas. It is important, however, to recognize that not all
individuals living in suitable areas will necessarily be exposed to Zika.
We still lack a great deal of basic epidemiological information about Zika. More needs to be
known about the species of mosquito that spreads the disease and how the Zika virus interacts with
related viruses such as dengue. As such information becomes available and clinical cases become
routinely diagnosed, the global evidence base will be strengthened, which will improve the accuracy
of future maps.
DOI: 10.7554/eLife.15272.002
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 2 of 19
Research Article Epidemiology and global health Microbiology and infectious disease
majority of symptomatic cases (Gatherer and Kohl, 2016). Considering these potentially severe
complications and the rapid expansion of ZIKV into previously unaffected areas, the global public
health community needs information about those areas that are environmentally suitable for trans-
mission of ZIKV to humans. Being a closely related flavivirus to DENV, there is furthermore the
potential for antigen-based diagnostic tests to exhibit cross-reactivity when IgM ELISA is used for
rapid diagnosis. Although ZIKV-specific serologic assays are being developed by the U.S. Centers
for Disease Control, currently the only method of confirming ZIKV infection is by using PCR on acute
specimens (Lanciotti et al., 2008, Faye et al., 2008). Awareness of suitability for transmission is
essential if proper detection methods are to be employed.
In this paper, we use species distribution modelling techniques that have been useful for mapping
other vector-borne diseases such as dengue (Bhatt et al., 2013), Leishmaniasis (Pigott et al.,
2014b), and Crimean-Congo Haemorrhagic Fever (Messina et al., 2015b) to map environmental
suitability for ZIKV. The environmental niche of a disease can be identified according to a combina-
tion of environmental conditions supporting its presence in a particular location, with statistical
modelling then allowing this niche to be described quantitatively (Kraemer et al., 2016). Niche
modelling uses records of known disease occurrence alongside hypothesized environmental covari-
ates to predict suitability for disease transmission in regions where it has yet to be reported
(Elith and Leathwick, 2009). Contemporary high spatial-resolution global data representing a
Figure 1. (A) Map showing the distribution of the final set of 323 ZIKV occurrence locations entered into the
ensemble Boosted Regression Tree modelling procedure. Locations are classified by year of occurrence to show
those which took place (i) prior to the 2007 outbreak in Federated States of Micronesia; (ii) between 2007–2014;
and (iii) during the 2015–2016 outbreak; (B) the total number of locations reporting symptomatic ZIKV occurrence
in humans globally over time.
DOI: 10.7554/eLife.15272.003
The following figure supplement is available for figure 1:
Figure supplement 1. Maps of all covariates entered into the 300 BRT models.
DOI: 10.7554/eLife.15272.004
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Research Article Epidemiology and global health Microbiology and infectious disease
Figure 2. Maps of (A) global environmental suitability for ZIKV, ranging from 0 (grey) to 1 (red), showing greater
detail for (B) the Americas, (C) Africa, and (D) Asia and Oceania.
DOI: 10.7554/eLife.15272.005
The following figure supplements are available for figure 2:
Figure supplement 1. Uncertainty around Zika suitability predictions displayed in main manuscript – Figure 2,
ranging from less than 0.01 (very little uncertainty) to 0.94 (greatest uncertainty).
DOI: 10.7554/eLife.15272.006
Figure supplement 2. Effect plots for each covariate entered into the ensemble of 300 BRT models.
DOI: 10.7554/eLife.15272.007
Figure 2 continued on next page
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Research Article Epidemiology and global health Microbiology and infectious disease
variety of environmental conditions allows for these predictions to be made at a global scale
(Hay et al., 2006).
Results
Figure 1A shows the locations of the 323 standardized occurrence records in the final dataset, classi-
fied by the following date ranges: (i) up until 2006 (before the outbreak in FSM); (ii) between 2007 (the
year of the FSM outbreak) and 2014; and (iii) since 2015, the first reporting of ZIKV in the Americas.
This map is accompanied by the graph in Figure 1B, showing the number of reported occurrence loca-
tions globally by year. These figures highlight the more sporadic nature of reporting until recent years,
with the majority of occurrences in the dataset (63%) coming from the recent 2015–2016 outbreak in
Latin America.
The final map that resulted from the mean of 300 ensemble Boosted Regression Tree (BRT) mod-
els is shown in Figure 2A (with greater detail shown for each region in Figures 2B–D). Figure 2—
figure supplement 1 shows the distribution of uncertainty based upon the upper and lower predic-
tion quantiles from the 300 models. We restricted our models to make predictions only within areas
where i) mosquito vectors (in this case Ae. aegypti) were able to persist and ii) where temperature
was sufficient for arboviral replication within the mosquito. The former of these was calculated by
taking the Ae. aegypti probability of occurrence (Kraemer et al., 2015c) value that incorporated
90% of all known occurrences (Kraemer et al., 2015b) (giving a threshold value of 0.8 and greater)
while the latter was evaluated using a mechanistic mosquito model (Brady et al., 2013;2014), which
identified regions where arboviral transmission could be sustained for at least 355 days (one year
Figure 2 continued
Figure supplement 3. Environmental suitability for Zika virus transmission to humans, not taking into account
temperature suitability for dengue via Aedes albopictus.
DOI: 10.7554/eLife.15272.008
Figure supplement 4. Map showing areas predicted to have greater dengue suitability (from Bhatt et al., 2013,
Nature) vs those which are predicted to have greater Zika suitability in the current study.
DOI: 10.7554/eLife.15272.009
Figure 3. Status of ZIKV reporting as of 2016 by country, showing countries that are highly environmentally suitable (having a suitable area of more than
10,000 square kilometres) but which have not yet reported symptomatic cases of ZIKV in humans. ’Currently reporting’ countries are those having
reported cases since 2015.
DOI: 10.7554/eLife.15272.010
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 5 of 19
Research Article Epidemiology and global health Microbiology and infectious disease
minus the human incubation period) in an average year. Figure 3 is a country-level map distinguish-
ing between those countries that are currently reporting ZIKV, those which have reported ZIKV in
the past, those which have highly suitable areas for transmission, and those which are unsuitable.
Our models predicted high levels of risk for ZIKV in many areas within the tropical and sub-tropical
zones. Large portions of the Americas are suitable for transmission, with the largest areas of risk
occurring in Brazil, followed by Colombia and Venezuela, all of which have reported high numbers of
cases in the 2015–2016 outbreak. In Brazil, where the highest numbers of ZIKV are reported in the
ongoing epidemic, the coastal cities in the south as well as large areas of the north are identified to
have the highest environmental suitability of ZIKV. The central region of Brazil, on the other hand,
has low population densities and smaller mosquito populations, which is reflected in the relatively
low suitability for ZIKV transmission seen in the map. Although ZIKV has yet to be reported in the
USA, a large portion of the southeast region of the country, including much of Texas through to Flor-
ida, is also highly suitable for transmission. Potential risk for ZIKV transmission is high in much of
sub-Saharan Africa, with continuous suitability in the Democratic Republic of Congo and surrounding
areas and several sporadic case reports in western sub-Saharan countries since the 1950s. Although
no symptomatic cases have yet been reported in India, a large portion of this country is at potential
risk for ZIKV transmission (over 2 million square kilometres), with environmental suitability extending
from its northwest regions through to Bangladesh and Myanmar. The Indochina region, southeast
China, and Indonesia all have large areas of environmental suitability as well, extending into Oce-
ania. While only representing less than ten percent of Australia’s total land area, the area shown to
be suitable for ZIKV transmission in its northernmost regions is considerable (comprising nearly
250,000 square kilometres).
Our models showed ZIKV risk to be particularly influenced by annual cumulative precipitation,
contributing 65.0% to the variation in the ensemble of models. The next most important predictor in
the model was temperature suitability for DENV transmission via Ae. albopictus, contributing 14.6%.
These are followed by urban extents (8.3%), temperature suitability for DENV via Ae. aegypti (5.7%),
the Enhanced Vegetation Index (EVI; 3.8%), and minimum relative humidity (2.5%). Effect plots for
each covariate are provided in Figure 2—figure supplement 2. Validation statistics indicated high
predictive performance of the BRT ensemble mean map evaluated in a 10-fold cross-validation pro-
cedure, with area under the receiver operating characteristic (AUC) of 0.829 ( ±0.121 SD). Due to
Table 1. Population living in areas suitable for ZIKV transmission within each major world region and
top four countries contributing to these populations at risk.
Region/Country Population living in areas suitable for ZIKV transmission (millions)
Africa 452.58
Nigeria 111.97
Democratic Republic of the Congo 68.95
Uganda 33.43
United Republic of Tanzania 22.70
Americas 298.36
Brazil 120.65
Mexico 32.22
Colombia 29.54
Venezuela 22.22
Asia 1,422.13
India 413.19
Indonesia 226.04
China 213.84
Bangladesh 133.29
World 2,173.27
DOI: 10.7554/eLife.15272.011
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Research Article Epidemiology and global health Microbiology and infectious disease
the uncertainty about Ae. albopictus as a competent vector for ZIKV, we also provide results for an
ensemble of models which did not include temperature suitability for dengue via this mosquito spe-
cies in Figure 2—figure supplement 3.
A threshold environmental suitability value of 0.397 in our final map was determined to incorpo-
rate 90% of all ZIKV occurrence locations. This was used to classify each 5 km x 5 km pixel on our
final map as suitable or unsuitable for ZIKV transmission to humans. Using high-resolution global
population estimates (WorldPop, 2015;SEDAC, 2015), we summed the populations living in Zika-
suitable areas and have identified 2.17 billion people globally living within areas that are environ-
mentally suitable for ZIKV transmission. Table 1 shows a breakdown of this figure by major world
region, also showing the top four contributing countries to the potential population at risk. Asia has
the most people living in areas that are suitable for ZIKV transmission at 1.42 billion, accounted for
in large part by those living in India. In Africa, roughly 453 million people are living in areas suitable
for ZIKV transmission, the largest proportion of which live in Nigeria. In the Americas, more than 298
million people live in ZIKV-suitable transmission zones, with approximately 40 percent of these peo-
ple living in Brazil. Within the majority of environmentally suitable areas for ZIKV in the Americas,
prolonged year-round transmission is possible. Southern Brazil and Argentina, however, are more
likely to see transmission interrupted throughout the year, as is the case with the USA should autoch-
thonous ZIKV transmission occur there. Using high-resolution data on births for the year 2015
(WorldPop, 2015), we also estimate that 5.42 million births will occur in the Americas over the next
year within areas and times of environmental suitability for ZIKV transmission.
Discussion
A large number of viruses (circa 219) are known to be pathogenic (Woolhouse et al., 2012). Of the
53 species of Flavivirus, 19 are reported to have caused illness in humans (ICTV, 2014). Some flavivi-
ruses, such as DENV, YFV, Japanese encephalitis virus, and West Nile virus, are widespread, causing
many thousands of infections each year. The remainder, however, have been recognized as being
pathogenic to humans for decades, but have highly focal reported distributions and are only minor
contributors to mortality and disability globally (Hay et al., 2013;Murray et al., 2015). As a result,
many are of relatively low priority when research and policy interest are considered (Pigott et al.,
2015b). The recent spread of ZIKV across the globe highlights the need to reassess our consider-
ation of these other flaviviruses, to gain a better understanding of the factors driving their spread
and the potential for geographic expansion beyond their currently limited geographical extents.
Environmental suitability for virus transmission in an area does not necessarily mean that it will
arrive and/or establish in that location. Arboviral infections in particular are dependent on a variety
of non-environmental factors, with their movement having historically been largely attributed to
human mobility from travel, trade, and migration, which introduce the viruses to places where mos-
quito vectors are already present (Murray et al., 2013;Weaver and Reisen, 2010;Nunes et al.,
2015;Gubler and Clark, 1995). The identification of locations with permissible environments for
transmission of emerging diseases like ZIKV is crucial, as importation could give rise to subsequent
autochthonous cases in these locations (Hennessey et al., 2016;Zanluca et al., 2015). In order to
identify places potentially receptive for ZIKV, we assembled the first comprehensive spatial dataset
for ZIKV occurrence in humans and compiled a comprehensive set of high-resolution environmental
covariates. We then used these data to implement a species distribution modelling approach
(Elith and Leathwick, 2009) that has proven useful for mapping other vector-borne diseases
(Bhatt et al., 2013;Pigott et al., 2014a;Mylne et al., 2015;Messina et al., 2015b), allowing us to
make inferences about environmental suitability for ZIKV transmission in areas where it has yet to be
reported or where we are less certain about its presence. How the ongoing epidemic unfolds in
terms of case numbers (or incidence) will depend on a range of other factors such as local transmis-
sion dynamics, herd immunity, patterns of contact among mosquitoes and infectious and susceptible
humans (Stoddard et al., 2013), and mosquito-to-human ratios as recently shown for dengue
(Kraemer et al., 2015a) and chikungunya (Salje et al., 2016).
Globally, we predict that over 2.17 billion people live in areas that are environmentally suitable
for ZIKV transmission. We also estimate the number of births occurring in the Americas only, as it is
the region for which the most accurate high-resolution population data on births exists
(Tatem et al., 2014;Sorichetta et al., 2015) and because it is the focus of an ongoing outbreak,
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Research Article Epidemiology and global health Microbiology and infectious disease
which is the largest recorded thus far. In the Americas alone, an estimated 5.42 million births
occurred in 2015 within areas and at times that are suitable for ZIKV transmission. It is important to
recognize that not all individuals will be exposed to ZIKV. Like with other flaviviruses, a ZIKV out-
break may be temporally and spatially sporadic and, even in the most receptive environments, is
unlikely that all of the population will be infected. Furthermore, increasing herd immunity of this
likely sterilizing infection will rapidly reduce the size of the susceptible population at risk for infection
in subsequent years (Dick et al., 1952) and work is ongoing to predict the likely infection dynamics
after establishment. Instead, the estimates are intended as indicators of the total number of individu-
als or births that may require protection during the first wave of the outbreak. Specifically, these
populations should be the focus of efforts to increase awareness and provide guidelines for mitigat-
ing personal risk of infection. In future analyses, our estimates could be extended to include ZIKV
incidence and the virus’ effect on incidence of associated conditions such as Guillain-Barre´ syndrome
and microcephaly. Before appropriately caveated estimates can be generated, however, more infor-
mation is needed regarding: (i) the background rate of these conditions due to other causes; (ii) how
risk may vary throughout the course of a pregnancy; (iii) the proportion of the population exposed
during outbreaks; and (iv) whether or not immunity acquired through a mother’s prior exposure is
protective.
For all arboviral diseases, public health education about reducing populations and avoiding con-
tact with mosquito vectors is required in at-risk areas. Specific to ZIKV is the risk of microcephaly in
newborns, which has led public health agencies to issue warnings for women who are currently or
planning on becoming pregnant in areas suspected to have ongoing ZIKV transmission and the dec-
laration of a Public Health Emergency of International Concern (Heymann et al., 2016). Due to the
sensitive nature and implications of these warnings, it is important that levels of risk are rigorously
estimated, validated, and updated. Transmission of related arboviral diseases still occurs in many
areas we defined as at-risk for ZIKV, which highlights the need for improved vector control out-
comes, particularly those targeting Ae. aegypti. Predicted levels of risk for ZIKV transmission are
potentially helpful for prioritized allocation of vector control resources, as well as for differential
diagnosis and, if a vaccine becomes available, delivery efforts. It should be noted that instances of
ZIKV sexual transmission have been reported (Patino-Barbosa et al., 2015;Musso et al., 2015b;
Foy et al., 2011a). We did not incorporate secondary modes of transmission into the models we
described here, but our map can help inform future discussions about the potential impact of this
mode of transmission as its relative importance becomes better understood.
A great deal of basic epidemiological information specific to ZIKV is lacking. As a result, informa-
tion must be leveraged from our knowledge about transmission of related arboviruses. Previous
work has focused on mapping other vector borne diseases that share much of the ecology of Zika,
such as DENV (Bhatt et al., 2013) and CHIKV, as well as for its primary vectors, Ae. aegypti and Ae.
albopictus (Kraemer et al., 2015c). For this reason, temperature suitability for dengue (Brady et al.,
2013,2014) was entered into the models due to the greater number of field and laboratory studies
available for parameterising this metric for DENV. Until more studies related to vector competence
and temperature constraints on ZIKV transmission to humans are conducted, this is the most accu-
rate indicator of arboviral disease transmission via Aedes mosquitoes currently available. Indeed, all
other covariates in our models could equally be applied to mapping DENV and CHIKV, and ZIKV-
specific refinements to modelling covariates will be possible as the disease continues to expand to
allow for improvements in future iterations of the map. The relatively smaller amount of occurrence
data available for ZIKV (especially prior to recent outbreaks) means that this dataset should also be
updated with new information as necessary, leading to a stronger global evidence base and
improved accuracy of future maps. Better understanding of ZIKV transmission dynamics will eventu-
ally allow for further cartographic refinements to be made, such as the differentiation between
endemic- and epidemic-prone areas. Still, all covariates included in the current study have been
updated and refined since (Bhatt et al., 2013), and when combined with the most extensive occur-
rence database available for ZIKV, the resulting map we present here is currently the most accurate
depiction of the distribution of environmental suitability for ZIKV. A map highlighting differences in
predicted suitability for both diseases is provided in Figure 2—figure supplement 4.
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Research Article Epidemiology and global health Microbiology and infectious disease
Conclusion
In this study, we produced the first global high spatial-resolution map of environmental suitability for
ZIKV transmission to humans using an assembly of known records of ZIKV occurrence and environ-
mental covariates in a species distribution modelling framework. While it is clear that much remains
to be understood about ZIKV, this first map serves as a baseline for understanding the change in the
geographical distribution of this globally emerging arboviral disease. Knowledge of the potential
distribution can encourage more vigilant surveillance in both humans and Aedes mosquito popula-
tions, as well as help in the allocation of limited resources for disease prevention. Public health
awareness campaigns and advice for mitigation of individual risk can also be focused in the areas we
have predicted to be highly suitable for ZIKV transmission, particularly during the first wave of infec-
tion in a population. The maps we have presented may also inform existing travel advisories for
pregnant women and other travellers. The maps and underlying data are freely available online via
figshare (http://www.figshare.com).
Materials and methods
To map environmental suitability for ZIKV transmission to humans, we applied a species distribution
modelling approach to establish a multivariate empirical relationship between the probability of
ZIKV occurrence and the environmental conditions in locations where the disease has been con-
firmed. We employed an ensemble boosted regression trees (BRT) methodology (De’ath, 2007;
Elith et al., 2008), which required the generation of: (i) a comprehensive compendium of known
locations of disease occurrence in humans; (ii) a set of background points representing locations
where ZIKV has not yet been reported; and (iii) a set of high-resolution globally gridded environmen-
tal and socioeconomic covariates hypothesised to affect ZIKV transmission. The resulting model pro-
duces a 5 x 5 km spatial-resolution global map of environmental suitability for ZIKV transmission to
humans.
Assembly of the geo-referenced ZIKV occurrence dataset
Information about the locations of ZIKV occurrence in humans was extracted from peer-reviewed lit-
erature, case reports, and informal online sources following previously established protocols
(Kraemer et al., 2015b;Messina et al., 2014;2015a). To collate the peer-reviewed dataset, litera-
ture searches were undertaken using PubMed (http://www.ncbi.nlm.nih.gov/pubmed) and ISI Web
of Science (http://www.webofknowledge.com) search engines using the search term ’Zika’. No lan-
guage restrictions were placed on these searches; however, only those citations with a full title and
abstract were retrieved, resulting in the review of 148 references ranging in publication dates
between 1951 and 2015. In-house language skills allowed review of all English, French, Portuguese
and Spanish articles for useable location information for human ZIKV occurrence. ProMED-mail
(http://www.promedmail.org) was also searched using the term ’Zika’, resulting in the review of 139
reports between 27 June 2007 and 18 January 2016. Additionally, the most current database of
ZIKV case locations in Brazil was obtained directly from the Brazilian Ministry of Health. From all
sources, only laboratory confirmation of symptomatic ZIKV infection in humans was entered into the
dataset (mention of suspected cases was not entered). Serological evidence from healthy individuals
could represent a past infection, with transmission potentially occurring in a different location to that
where the individual currently resides (Darwish et al., 1983), or could be an artefact from possible
cross-reactivity with a variety of different viruses (Smithburn et al., 1954). As a result, these less reli-
able diagnoses of ZIKV were excluded.
All available location information was extracted from each peer-reviewed article and ProMED
case report. The site name was used together with all contextual information provided about the
site to determine its latitudinal and longitudinal coordinates using Google Maps (https://www.maps.
google.com). If the study site could be geo-positioned to a specific place, it was recorded as a point
location. If the study site could only be identified at an administrative area level (e.g. province or dis-
trict), it was recorded as a polygon along with an identifier of its administrative unit. If imported
cases were reported with information on the site of infection, they were geo-positioned to this site;
if imported cases were reported with no information about the site of infection, they were not
entered into the dataset. Informal online data sources were collated automatically by the web-based
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 9 of 19
Research Article Epidemiology and global health Microbiology and infectious disease
system HealthMap (http://www.healthmap.org) as described elsewhere (Freifeld et al., 2008). Alerts
for ZIKV were obtained from HealthMap for the years 2014–2016, and then manually checked for
validity. In total, usable location information was extracted from 110 sources. Information was also
collected about the status of symptoms in each reported occurrence, distinguishing between those
where symptomatic cases were being reported, versus those where only seroprevalence was
detected in healthy individuals.
Due to the potential for multiple independent reports referring to the same cases temporal and
spatial standardization was required, as we have described previously in detail for dengue mapping
efforts (Messina et al., 2014). In brief, an occurrence was defined as a unique location with one or
more confirmed cases of ZIKV occurring within one calendar year (the finest temporal resolution
available across all records). Point locations were considered to be overlapping if they lay on the
same 5 km x 5 km pixel, and polygon locations were identified by a unique administrative unit code.
Furthermore, all polygons whose geographic area was greater than one square decimal degree
(approximately 111 square kilometers at the equator) were removed from the dataset to avoid aver-
aging covariate values over very large areas, and only those occurrences comprising symptomatic
individuals were retained for modelling purposes to ensure an accurate location of infection. In total,
the final occurrence dataset contained 323 unique occurrences to be entered into our BRT modelling
procedure. A map of the final set of occurrence locations is provided as Figure 1A.
Generation of the background location dataset
Separate maps of the relative probability of occurrence of Ae. aegypti and Ae. albopictus
(Kraemer et al., 2015c) were used to compute a combined metric of the relative probability of vec-
tor occurrence, by taking the maximum value from the two layers for all 5 km x 5 km gridded cells
globally. The inverse of this combined-Aedes occurrence probability layer (higher values indicating
greater certainty of absence) was then used to draw a biased sample of 10,000 background loca-
tions. As such, a greater number of background points were sampled in areas where we are more
certain that Ae. aegypti or Ae. albopictus do not occur, and therefore where ZIKV is less likely to be
transmitted to humans. While it has been demonstrated that predictive accuracy from presence-
background species distribution models can be improved by biasing background record locations
toward areas with greatest reporting probabilities (Phillips et al., 2009), information on possible
reporting biases, or proxies of such spatial bias, are currently unavailable for ZIKV. These 10,000
background locations were combined with the standardized occurrence dataset to serve as compari-
son data locations in the BRT species distribution modelling procedure. The background locations
were weighted such that their total sum was equal to the total number of occurrence locations
(n=237; pseudo-absence weighting=0.0237), in order to aid in the discrimination capacity of the
model (Barbet-Massin et al., 2012).
Explanatory covariates
A set of six covariates hypothesized to influence the global distribution of ZIKV transmission to
humans were used in our models to establish an empirical relationship between ZIKV presence or
absence and underlying environmental conditions. These six covariates included: (i) an index of tem-
perature suitability for dengue transmission to humans via Ae. aegypti; (ii) temperature suitability for
dengue transmission to humans via Ae. albopictus; (iii) minimum relative humidity; (iv) annual cumu-
lative precipitation; (v) an enhanced vegetation index (EVI); and (vi) urban versus rural habitat type.
The underlying hypothesis behind each of the covariates is discussed in more detail below, along
with a description of data sources and any processing that was undertaken before entering these
covariates into our models. Maps of each covariate layer are provided in the supplementary informa-
tion in Figure 1—figure supplement 1.
Temperature suitability for dengue transmission to humans
via Ae. aegypti or Ae. albopictus: Temperature affects key physiological processes in Aedes mosqui-
toes, including age- and temperature-dependent adult female survival, as well as the duration of the
extrinsic incubation period (EIP) of arboviruses and the length of the gonotrophic cycle (Brady et al.,
2013). While these parameters have yet to be measured experimentally for ZIKV, they have been for
the closely related DENV. We obtained temperature data from WorldClim v1.03 (http://www.
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 10 of 19
Research Article Epidemiology and global health Microbiology and infectious disease
wordclim.org), which uses historic global meteorological station data from 1961–2005 to interpolate
global climate surfaces. MARKSIM software (Jones and Thornton, 2000) was then used to apply the
coefficients of 17 Global Climate Models (GCMs) to estimate temperature values for the year 2015.
This enabled us to incorporate the quantified effects of temperature on DENV transmission into a
cohort simulation model that analysed the cumulative effects of both diurnal and inter-seasonal
changes in temperature on DENV transmission within an average year, both for Ae. aegypti and Ae.
albopictus separately. The models were then applied to the 2015 temperature data for each 5 km x
5 km grid cell globally. This resulted in maps of temperature suitability for DENV transmission by
either Aedes species ranging from 0 (no suitable days) to 1 (365 suitable days). These measures
were then used as a proxy for temperature suitability for ZIKV transmission to humans.
Annual cumulative precipitation
Presence of static surface water in natural or man-made containers is a pre-requisite for Aedes ovi-
position and larval and pupal development. While fine-scale spatial and temporal heterogeneities
have been observed between precipitation, vector abundance, and incidence of human DENV infec-
tions, there is evidence that areas with greater amounts of precipitation are generally associated
with higher DENV infection risk (Chandy et al., 2013;Chowell and Sanchez, 2006;Dom et al.,
2013;Pinto et al., 2011;Restrepo et al., 2014;Sang et al., 2014;Sankari et al., 2012;
Campbell et al., 2015). Although studies that directly connect levels of precipitation to ZIKV trans-
mission have yet to exist, we assumed for Zika a similar association of precipitation as closely related
flaviruses. WorldClim v1.03 precipitation data and MARKSIM software were used as described above
for temperature, to estimate annual cumulative precipitation for the year 2015 for each 5 km x 5 km
grid cell globally.
Minimum relative humidity
Greater relative humidity has been found to promote DENV propagation in Ae. aegypti mosquitoes
in several localized settings (Colo
´n-Gonza
´lez et al., 2011;Thu et al., 1998), and has also been
found to be an important contributor when predicting DENV risk at a global scale (Hales et al.,
2002). Therefore, we again assumed a similar association for ZIKV in the absence of any direct stud-
ies, and included the minimum annual relative humidity in our models as a potential limiting factor
to ZIKV transmission. Relative humidity (RH) was calculated as a percent of saturation humidity, or
the amount of water vapour required to saturate the air given a particular temperature, using the
temperature data from WorldClim v1.03 described earlier. The saturation, or ’dew’, point (Tdew Þwas
calculated using a tabular relationship (Linacre, 1977). RH was then calculated as follows:
RH ¼VðTxÞ
VðTdewÞ100
Where VðTdewÞ=611:21 exp 17:502 T
240:97þT
and VðTxÞis the humidity at the given tempera-
ture. We then extracted the minimum annual RH for each 5 km x 5 km pixel globally for the year
2015.
Enhanced vegetation index (EVI)
A close association has been shown between local moisture supply, vegetation canopy develop-
ment, and abundance of mosquito reproduction (Linthicum et al., 1999), with previous studies
highlighting the importance of moisture-related measures such as relative humidity to DENV occur-
rence (Hales et al., 2002). Although resistant to desiccation, both Aedes eggs and adults require
moisture to survive (Cox et al., 2007;Sota and Mogi, 1992;Reiskind and Lounibos, 2009;
Costa et al., 2010;Luz et al., 2008), with low dry season moisture levels substantially affecting
Aedes mortality (Russell et al., 2001;Trpis, 1972;Luz et al., 2008). Vegetation canopy cover has
previously been associated with higher Aedes larvae density (Fuller et al., 2009;Troyo et al., 2009;
Bisset Lazcano et al., 2006;Barrera et al., 2006) by reducing evaporation from containers,
decreasing sub-canopy wind speed, and protecting outdoor habitats from direct sunlight. To
account for these factors, we included a 5 km x 5 km resolution measure of the EVI derived from
NASA’s Moderate Resolution Imaging Spectrometer (MODIS, Boulder, Colorado) imagery
(Wan et al., 2002;Lin, 2012), summarized from gap-filled, 8-day, 1 km x 1 km resolution images
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 11 of 19
Research Article Epidemiology and global health Microbiology and infectious disease
acquired globally for years 2000 through 2014 (Weiss et al., 2014) to produce a mean annual EVI
layer. This mean EVI product is indicative of amount of photosynthesis taking place in the environ-
ment over the course of a year, which is positively correlated with the density of vegetation, and is
thus a proxy for the level of moisture available given the relationship between precipitation and veg-
etative growth.
Urban versus rural habitat type
There is a well-established link between urban areas, some vector borne diseases, and their vectors.
In particular, Ae. aegypti is found in close proximity to human dwellings often breeding in artificial
containers (Brown et al., 2011;Powell and Tabachnick, 2013;Kraemer et al., 2015c). To identify
the relationship between urbanisation and ZIKV presence we adapted probabilistic spatial modelling
techniques to predict the spatial distribution of global urban extents at a 5 km x 5 km spatial resolu-
tion. We used urban growth rates from the United Nations Population Division (Division, 2014),
paired with urban extents measured and tested by the Moderate Resolution Imaging Spectroradi-
ometer Collection 5 (MODIS C5) land-cover product for Asia (Schneider et al., 2009;
2010;2015). A set of spatial covariate datasets hypothesized to influence the spatial patterns of
urban expansion was generated, including the time to travel from each 5 km x 5 km pixel to a major
city (Nelson, 2008), the proportion of urbanised land within a buffer of 20 km, human population
density (Linard and Tatem, 2012;Stevens et al., 2015;Gaughan et al., 2013), slope (Becker et al.,
2009), and distance to water (Arino et al., 2008). A BRT modelling approach was then used to pre-
dict areas that would become urban in 2015 (Linard et al., 2013). Outputs were tested against a
training dataset comprising points from Asia only, and showed good overall predictive performance
(AUC=0.82). The output raster is a 5 km x 5 km gridded surface with urban (1) vs. rural (0) pixels.
Ensemble boosted regression trees approach
The boosted regression tree (BRT) modelling procedure combines regression trees with gradient
boosting (Friedman, 2001). In this procedure, an initial regression tree is fitted and iteratively
improved upon in a forward stagewise manner (boosting) by minimising the variation in the response
not explained by the model at each iteration. It has been shown to fit complicated response func-
tions efficiently, while guarding against over-fitting by use of extensive internal cross-validation. As
such, this approach has been successfully employed in the past to map dengue and its Aedes mos-
quito vectors, as well as other vector-borne diseases (Bhatt et al., 2013;Pigott et al., 2014b;
Messina et al., 2015b;Kraemer et al., 2015c). To increase the robustness of model predictions
and quantify model uncertainty, we fitted an ensemble (Arau´jo and New, 2007) of 300 BRT models
to separate bootstraps of the data. We then evaluated the central tendency as the mean across all
300 BRT models (Bhatt et al., 2013). Each of the 300 individual models was fitted using the gbm.
step subroutine in the dismo package in the R statistical programming environment (Elith et al.,
2008). All other tuning parameters of the algorithm were held at their default values (tree complex-
ity= 4, learning rate= 0.005, bag fraction= 0.75, step size= 10, cross-validation folds=10). Each of
the 300 models predicts environmental suitability on a continuous scale from 0 to 1, with a final pre-
diction map then being generated by calculating the mean prediction across all models for each
5 km x 5 km pixel. Cross-validation was applied to each model, whereby ten subsets of the data
comprising 10% of the presence and background observations were assessed based on their ability
to predict the distribution of the other 90% of records using the mean area under the curve (AUC)
statistic. This AUC value was then averaged across the ten sub-models and finally across all 300
models in the ensemble in order to derive an overall estimate of goodness-of-fit. Additionally, to
avoid AUC inflation due to spatial sorting bias, a pairwise distance sampling procedure was used,
resulting in a final AUC which is lower than would be returned by standard procedures but which
gives a more realistic quantification of the model’s ability to extrapolate predictions to new regions
(Wenger and Olden, 2012). We restricted our models to make predictions only within areas where
either Ae. aegypti probability of occurrence (Kraemer et al., 2015c) is more than 0.8 or temperature
is conducive to transmission for at least 355 days in an average year. A second ensemble of 300
models was executed which did not take into account temperature suitability for dengue transmis-
sion via Ae. albopictus, due to the uncertainty of this species as a competent ZIKV vector. The results
of this ensemble of models are provided in Figure 2—figure supplement 3.
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 12 of 19
Research Article Epidemiology and global health Microbiology and infectious disease
Population and births at risk
To calculate the number of people located in an area that is at any level of risk for ZIKV transmission,
the global ZIKV environmental suitability map was combined with fine-scale global population surfa-
ces (SEDAC, 2015;WorldPop, 2015). Firstly, the continuous ZIKV environmental suitability map
(ranging from 0 to 1) was converted into a binary surface indicating whether there is any risk of trans-
mission. To do this, we carried out a protocol previously used in (Pigott et al., 2015a), choosing a
threshold environmental suitability value that encompasses 90% of the ZIKV occurrence point loca-
tions. This threshold cut-off of 90% was chosen (rather than 100%) to reflect potential errors or inac-
curate locations in the occurrence point dataset. Every 5 km x 5 km pixel in the suitability map with a
value above this threshold value was considered at risk for ZIKV transmission. Finally, to estimate the
population at risk, we multiplied this binary ZIKV risk map by the global population counts (aligned
and aggregated to the same 5 x 5 km grid) for the year 2015 and summed across all cells.
We next estimated the maximum number of births potentially affected by ZIKV in Latin America,
as this region is the focus of the recent outbreak and the first to point to a possible association with
microcephaly in newborn infants to mothers infected with ZIKV. In order to do this, we first identified
the proportion of the year that is suitable for ZIKV transmission within areas that are predicted to be
suitable in the binary ZIKV risk map. This proportion was derived from existing temperature suitabil-
ity models (Brady et al., 2013;2014), which predict the total number of days within an average year
that arbovirus transmission can be sustained in Ae. aegypti, assuming there is a local human reser-
voir of infection. While the intra-mosquito viral dynamics in this model were parameterised for den-
gue virus, the limited information currently available on other arboviruses suggests that their
dynamics are similar (Lambrechts et al., 2011). Using the resulting 5 km x 5 km map showing the
proportion of the year suitable for ZIKV transmission to humans, we then multiplied this by a map
(also at a 5 km x 5 km resolution) of the number of births in the Americas for the year 2015, updated
from (Tatem et al., 2014;UNFPA, 2014). The resulting map indicates the number of births in the
Americas potentially at risk for ZIKV (for 2015), assuming ZIKV currently fully occupies its environ-
mental niche and that births are evenly distributed throughout the year.
Acknowledgements
We thank the Secretariat of Health Surveillance, Ministry of Health of Brazil for providing access to
the geographical coordinates of occurrence. JPM, MUGK and TJ receive, and OJB and SIH acknowl-
edge funding from the International research Consortium on Dengue Risk Assessment Management
and Surveillance (IDAMS; European Commission 7
th
Framework Programme (21893)). OJB and SIH
are supported by the Bill & Melinda Gates Foundation (OPP1053338). SIH is also funded by a Senior
Research Fellowship from the Wellcome Trust (095066), and grants from the Bill & Melinda Gates
Foundation (OPP1119467, OPP1106023 and OPP1093011). DMP is also funded by the Bill &
Melinda Gates Foundation (OPP1093011). DJW and PWG receive support from the Bill and Melinda
Gates Foundation (OPP1068048, OPP1106023). NG is supported by a University of Melbourne
McKenzie fellowship. CWR is funded through the University of Southampton’s Economic and Social
Research Council’s Doctoral Training Centre. TWS is supported by grants from the National Insti-
tutes of Health (P01AI098670) and the Bill and Melinda Gates Foundation (OPP1081737). EC and
JSB are supported by the National Library of Medicine of the National Institutes of Health
(R01LM010812).
Additional information
Competing interests
SIH: Reviewing editor, eLife. The other authors declare that no competing interests exist.
Funding
Funder Grant reference number Author
European Commission 21893 Jane P Messina
Moritz UG Kraemer
Thomas Jaenisch
Messina et al. eLife 2016;5:e15272. DOI: 10.7554/eLife.15272 13 of 19
Research Article Epidemiology and global health Microbiology and infectious disease
Bill and Melinda Gates
Foundation
OPP1053338; OPP1119467;
OPP1106023; OPP1093011;
OPP1081737; OPP1068048
Oliver J Brady
David M Pigott
Daniel J Weiss
Thomas W Scott
Simon I Hay
Wellcome Trust 095066 Simon I Hay
University of Southampton Economic and Social
Research Council’s Doctoral
Training Centre
Corrine W Ruktanonchai
National Institutes of Health P01AI098670 Thomas W Scott
University of Melbourne McKenzie fellowship Nick Golding
The funders had no role in study design, data collection and interpretation, or the decision to
submit the work for publication.
Author contributions
JPM, DMP, SIH, Conception and design, Acquisition of data, Analysis and interpretation of data,
Drafting or revising the article; MUGK, OJB, NG, Conception and design, Analysis and interpretation
of data, Drafting or revising the article; FMS, PWG, Conception and design, Acquisition of data,
Drafting or revising the article; DJW, Acquisition of data, Analysis and interpretation of data, Draft-
ing or revising the article; CWR, EC, AJT, Acquisition of data, Analysis and interpretation of data;
JSB, CJLM, Acquisition of data, Drafting or revising the article; KK, TJ, TWS, Analysis and interpreta-
tion of data, Drafting or revising the article; FM, Acquisition of data, Contributed unpublished essen-
tial data or reagents
Author ORCIDs
Jane P Messina, http://orcid.org/0000-0001-7829-1272
Moritz UG Kraemer, http://orcid.org/0000-0001-8838-7147
Simon I Hay, http://orcid.org/0000-0002-0611-7272
Additional files
Major datasets
The following datasets were generated:
Author(s) Year Dataset title Dataset URL
Database, license,
and accessibility
information
Jane P Messina,
Freya M Shearer
2016 Global compendium of human Zika
virus occurrence
http://dx.doi.org/10.
6084/m9.figshare.
2573629
Publicly available at
figshare
Jane P Messina,
Moritz UG Kraemer,
Oliver J Brady, Da-
vid M Pigott, Freya
M Shearer, Daniel J
Weiss, Nick Gold-
ing, Corrine W Ruk-
tanonchai, Emily
Cohn, John S
Brownstein, Kamran
Khan, Andrew J Ta-
tem, Thomas Jae-
nisch, Thomas W
Scott, Simon I Hay
2016 Environmental suitability for Zika
virus transmission
http://dx.doi.org/10.
6084/m9.figshare.
2574298
Publicly available at
figshare
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