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.

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 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 combined 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 vector 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 associations with Guillain-Barré 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 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 transmission 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 combination 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 covariates 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 variety of environmental conditions allows for these predictions to be made at a global scale (Hay et al., 2006).

Figure 1A shows the locations of the 323 standardized occurrence records in the final dataset, classified 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 locations 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.

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The final map that resulted from the mean of 300 ensemble Boosted Regression Tree (BRT) models 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 prediction 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 minus the human incubation period) in an average year. Figure 3 is a country-level map distinguishing 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 Florida, 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 Oceania. 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).

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Figure 3

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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 procedure, with area under the receiver operating characteristic (AUC) of 0.829 ( ± 0.121 SD). Due to 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 species in Figure 2—figure supplement 3.

A threshold environmental suitability value of 0.397 in our final map was determined to incorporate 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 environmentally 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 people 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 autochthonous 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.

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 flaviviruses, 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 consideration 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 mosquito 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 transmission 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, 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 outbreak 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 individuals 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 mitigating 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-Barré syndrome and microcephaly. Before appropriately caveated estimates can be generated, however, more information 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 contact 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 declaration 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 outcomes, 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, information 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 accurate 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 eventually 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 occurrence 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.

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 confirmed. 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 environmental and socioeconomic covariates hypothesised to affect ZIKV transmission. The resulting model produces a 5 x 5 km spatial-resolution global map of environmental suitability for ZIKV transmission to humans.

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Author details

1. Jane P Messina

   Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   JPM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   jane.messina@zoo.ox.ac.uk

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0001-7829-1272

2. Moritz UG Kraemer

   Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   MUGK, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0001-8838-7147

3. Oliver J Brady

   Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   OJB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

4. David M Pigott

   1. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
   2. Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States

   Contribution

   DMP, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

5. Freya M Shearer

   Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   FMS, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

6. Daniel J Weiss

   Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   DJW, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

7. Nick Golding

   Department of BioSciences, University of Melbourne, Parkville, United Kingdom

   Contribution

   NG, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

8. Corrine W Ruktanonchai

   WorldPop project, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom

   Contribution

   CWR, Acquisition of data, Analysis and interpretation of data

   Competing interests

   No competing interests declared.

9. Peter W Gething

   Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   PWG, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

10. Emily Cohn

    Boston Children's Hospital, Harvard Medical School, Boston, United Kingdom

    Contribution

    EC, Acquisition of data, Analysis and interpretation of data

    Competing interests

    No competing interests declared.

11. John S Brownstein

    Boston Children's Hospital, Harvard Medical School, Boston, United Kingdom

    Contribution

    JSB, Acquisition of data, Drafting or revising the article

    Competing interests

    No competing interests declared.

12. Kamran Khan

    1. Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
    2. Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Canada

    Contribution

    KK, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    No competing interests declared.

13. Andrew J Tatem

    1. WorldPop project, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
    2. Flowminder Foundation, Stockholm, Sweden

    Contribution

    AJT, Acquisition of data, Analysis and interpretation of data

    Competing interests

    No competing interests declared.

14. Thomas Jaenisch

    1. Section Clinical Tropical Medicine, Department for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
    2. German Centre for Infection Research (DZIF), Heidelberg partner site, Heidelberg, Germany

    Contribution

    TJ, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    No competing interests declared.

15. Christopher JL Murray

    Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States

    Contribution

    CJLM, Acquisition of data, Drafting or revising the article

    Competing interests

    No competing interests declared.

16. Fatima Marinho

    Secretariat of Health Surveillance, Ministry of Health Brazil, Brasilia, Brazil

    Contribution

    FM, Acquisition of data, Contributed unpublished essential data or reagents

    Competing interests

    No competing interests declared.

17. Thomas W Scott

    Department of Entomology and Nematology, University of California Davis, Davis, United States

    Contribution

    TWS, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    No competing interests declared.

18. Simon I Hay

    1. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
    2. Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States

    Contribution

    SIH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    sihay@uw.edu

    Competing interests

    SIH: Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0002-0611-7272

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