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.

Figure 1 with 1 supplement see all

Download asset Open asset

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).

Figure 2 with 4 supplements see all

Download asset Open asset

Figure 3

Download asset Open asset

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.

1. 1. Jane P Messina
   2. Freya M Shearer

   (2016) Global compendium of human Zika virus occurrence

   Publicly available at figshare.

   http://dx.doi.org/10.6084/m9.figshare.2573629
2. 1. Jane P Messina
   2. Moritz UG Kraemer
   3. Oliver J Brady
   4. David M Pigott
   5. Freya M Shearer
   6. Daniel J Weiss
   7. Nick Golding
   8. Corrine W Ruktanonchai
   9. Emily Cohn
   10. John S Brownstein
   11. Kamran Khan
   12. Andrew J Tatem
   13. Thomas Jaenisch
   14. Thomas W Scott
   15. Simon I Hay

   (2016) Environmental suitability for Zika virus transmission

   Publicly available at figshare.

   http://dx.doi.org/10.6084/m9.figshare.2574298

1. 1. Araújo MB
   2. New M

   (2007) Ensemble forecasting of species distributions

   Trends in Ecology & Evolution 22:42–47.

   https://doi.org/10.1016/j.tree.2006.09.010

   • Google Scholar
2. 1. Arino O
   2. Bicheron P
   3. Achard F
   4. Latham J
   5. Witt R
   6. Weber JL

   (2008)

   GLOBCOVER the most detailed portrait of earth

   ESA Bulletin pp. 24–31.

   • Google Scholar
3. 1. Barbet-Massin M
   2. Jiguet F
   3. Albert CH
   4. Thuiller W

   (2012) Selecting pseudo-absences for species distribution models: How, where and how many?

   Methods in Ecology and Evolution 3:327–338.

   https://doi.org/10.1111/j.2041-210X.2011.00172.x

   • Google Scholar
4. 1. Barrera R
   2. Amador M
   3. Clark GG

   (2006)

   Use of the pupal survey technique for measuring Aedes aegypti (Diptera: Culicidae) productivity in Puerto Rico

   The American Journal of Tropical Medicine and Hygiene 74:290–302.

   • Google Scholar
5. 1. Becker JJ
   2. Sandwell DT
   3. Smith WHF
   4. Braud J
   5. Binder B
   6. Depner J
   7. Fabre D
   8. Factor J
   9. Ingalls S
   10. Kim S-H
   11. Ladner R
   12. Marks K
   13. Nelson S
   14. Pharaoh A
   15. Trimmer R
   16. Von Rosenberg J
   17. Wallace G
   18. Weatherall P

   (2009) Global bathymetry and elevation data at 30 arc seconds resolution: Srtm30_plus

   Marine Geodesy 32:355–371.

   https://doi.org/10.1080/01490410903297766

   • Google Scholar
6. 1. Benedict MQ
   2. Levine RS
   3. Hawley WA
   4. Lounibos LP

   (2007) Spread of the tiger: Global risk of invasion by the mosquito aedes albopictus

   Vector Borne and Zoonotic Diseases 7:76–85.

   https://doi.org/10.1089/vbz.2006.0562

   • Google Scholar
7. 1. Bhatt S
   2. Gething PW
   3. Brady OJ
   4. Messina JP
   5. Farlow AW
   6. Moyes CL
   7. Drake JM
   8. Brownstein JS
   9. Hoen AG
   10. Sankoh O
   11. Myers MF
   12. George DB
   13. Jaenisch T
   14. Wint GR
   15. Simmons CP
   16. Scott TW
   17. Farrar JJ
   18. Hay SI

   (2013) The global distribution and burden of dengue

   Nature 496:504–507.

   https://doi.org/10.1038/nature12060

   • Google Scholar
8. 1. Bisset Lazcano JA
   2. Marquetti MC
   3. Portillo R
   4. Rodríguez MM
   5. Suárez S
   6. Leyva M

   (2006) Ecological factors linked to the presence of Aedes aegypti larvae in highly infested areas of Playa, a municipality belonging to Ciudad de La Habana, Cuba

   Revista Panamericana de Salud Pública 19:379–384.

   https://doi.org/10.1590/S1020-49892006000600003

   • Google Scholar
9. 1. Bogoch II
   2. Brady OJ
   3. Kraemer MU
   4. German M
   5. Creatore MI
   6. Kulkarni MA
   7. Brownstein JS
   8. Mekaru SR
   9. Hay SI
   10. Groot E
   11. Watts A
   12. Khan K

   (2016) Anticipating the international spread of zika virus from brazil

   Lancet 387:335–336.

   https://doi.org/10.1016/S0140-6736(16)00080-5

   • Google Scholar
10. 1. Brady OJ
    2. Golding N
    3. Pigott DM
    4. Kraemer MU
    5. Messina JP
    6. Reiner RC
    7. Scott TW
    8. Smith DL
    9. Gething PW
    10. Hay SI

    (2014) Global temperature constraints on aedes aegypti and ae. albopictus persistence and competence for dengue virus transmission

    Parasites & Vectors 7:338.

    https://doi.org/10.1186/1756-3305-7-338

    • Google Scholar
11. 1. Brady OJ
    2. Johansson MA
    3. Guerra CA
    4. Bhatt S
    5. Golding N
    6. Pigott DM
    7. Delatte H
    8. Grech MG
    9. Leisnham PT
    10. Maciel-de-Freitas R
    11. Styer LM
    12. Smith DL
    13. Scott TW
    14. Gething PW
    15. Hay SI

    (2013) Modelling adult aedes aegypti and aedes albopictus survival at different temperatures in laboratory and field settings

    Parasites & Vectors 6:351.

    https://doi.org/10.1186/1756-3305-6-351

    • Google Scholar
12. 1. Brown JE
    2. McBride CS
    3. Johnson P
    4. Ritchie S
    5. Paupy C
    6. Bossin H
    7. Lutomiah J
    8. Fernandez-Salas I
    9. Ponlawat A
    10. Cornel AJ
    11. Black WC
    12. Gorrochotegui-Escalante N
    13. Urdaneta-Marquez L
    14. Sylla M
    15. Slotman M
    16. Murray KO
    17. Walker C
    18. Powell JR

    (2011) Worldwide patterns of genetic differentiation imply multiple 'domestications' of aedes aegypti, a major vector of human diseases

    Proceedings of the Biological Sciences 278:2446–2454.

    https://doi.org/10.1098/rspb.2010.2469

    • Google Scholar
13. 1. Campbell KM
    2. Haldeman K
    3. Lehnig C
    4. Munayco CV
    5. Halsey ES
    6. Laguna-Torres VA
    7. Yagui M
    8. Morrison AC
    9. Lin CD
    10. Scott TW

    (2015) Weather regulates location, timing, and intensity of dengue virus transmission between humans and mosquitoes

    PLoS Neglected Tropical Diseases 9:e0003957.

    https://doi.org/10.1371/journal.pntd.0003957

    • Google Scholar
14. 1. Chandy S
    2. Ramanathan K
    3. Manoharan A
    4. Mathai D
    5. Baruah K

    (2013) Assessing effect of climate on the incidence of dengue in tamil nadu

    Indian Journal of Medical Microbiology 31:283–286.

    https://doi.org/10.4103/0255-0857.115640

    • Google Scholar
15. 1. Chowell G
    2. Sanchez F

    (2006)

    Climate-based descriptive models of dengue fever: the 2002 epidemic in Colima, Mexico

    Journal of Environmental Health 68:40–44.

    • Google Scholar
16. 1. Colón-González FJ
    2. Lake IR
    3. Bentham G

    (2011) Climate variability and dengue fever in warm and humid mexico

    The American Journal of Tropical Medicine and Hygiene 84:757–763.

    https://doi.org/10.4269/ajtmh.2011.10-0609

    • Google Scholar
17. 1. Costa E
    2. Santos EMM
    3. Correia JC
    4. Albuquerque CMR

    (2010) Impact of small variations in temperature and humidity on the reproductive activity and survival of aedes aegypti (diptera, culicidae)

    Rev Bras Entomol 54:488–493.

    https://doi.org/10.1590/s0085-56262010000300021

    • Google Scholar
18. 1. Cox J
    2. Grillet ME
    3. Ramos OM
    4. Amador M
    5. Barrera R

    (2007)

    Habitat segregation of dengue vectors along an urban environmental gradient

    The American Journal of Tropical Medicine and Hygiene 76:820–826.

    • Google Scholar
19. 1. Darwish MA
    2. Hoogstraal H
    3. Roberts TJ
    4. Ahmed IP
    5. Omar F

    (1983) A sero-epidemiological survey for certain arboviruses (togaviridae) in pakistan

    Transactions of the Royal Society of Tropical Medicine and Hygiene 77:442–445.

    https://doi.org/10.1016/0035-9203(83)90106-2

    • Google Scholar
20. 1. De'ath G

    (2007) Boosted trees for ecological modeling and prediction

    Ecology 88:243–251.

    https://doi.org/10.1890/0012-9658(2007)88[243:btfema]2.0.co;2

    • Google Scholar
21. 1. Dick GW
    2. Kitchen SF
    3. Haddow AJ

    (1952) Zika virus. I. isolations and serological specificity

    Transactions of the Royal Society of Tropical Medicine and Hygiene 46:509–520.

    https://doi.org/10.1016/0035-9203(52)90042-4

    • Google Scholar
22. 1. Dick GW

    (1953)

    Yellow fever; a problem in epidemiology

    British Medical Bulletin 9:215–235.

    • Google Scholar
23. 1. Dom NC
    2. Ahmad AH
    3. Latif ZA
    4. Ismail R
    5. Pradhan B

    (2013) Coupling of remote sensing data and environmental-related parameters for dengue transmission risk assessment in subang jaya, malaysia

    Geocarto International 28:258–272.

    https://doi.org/10.1080/10106049.2012.696726

    • Google Scholar
24. 1. Elith J
    2. Leathwick JR
    3. Hastie T

    (2008) A working guide to boosted regression trees

    The Journal of Animal Ecology 77:802–813.

    https://doi.org/10.1111/j.1365-2656.2008.01390.x

    • Google Scholar
25. 1. Elith J
    2. Leathwick JR

    (2009) Species distribution models: Ecological explanation and prediction across space and time

    Annu Rev Ecol Evol S 40:677–697.

    https://doi.org/10.1146/annurev.ecolsys.110308.120159

    • Google Scholar
26. 1. Faria NR
    2. Azevedo RS
    3. Kraemer MU
    4. Souza R
    5. Cunha MS
    6. Hill SC
    7. Thézé J
    8. Bonsall MB
    9. Bowden TA
    10. Rissanen I
    11. Rocco IM
    12. Nogueira JS
    13. Maeda AY
    14. Vasami FG
    15. Macedo FL
    16. Suzuki A
    17. Rodrigues SG
    18. Cruz AC
    19. Nunes BT
    20. Medeiros DB
    21. Rodrigues DS
    22. Nunes Queiroz AL
    23. da Silva EV
    24. Henriques DF
    25. Travassos da Rosa ES
    26. de Oliveira CS
    27. Martins LC
    28. Vasconcelos HB
    29. Casseb LM
    30. Simith DB
    31. Messina JP
    32. Abade L
    33. Lourenço J
    34. Alcantara LC
    35. de Lima MM
    36. Giovanetti M
    37. Hay SI
    38. de Oliveira RS
    39. Lemos PS
    40. de Oliveira LF
    41. de Lima CP
    42. da Silva SP
    43. de Vasconcelos JM
    44. Franco L
    45. Cardoso JF
    46. Vianez-Júnior JL
    47. Mir D
    48. Bello G
    49. Delatorre E
    50. Khan K
    51. Creatore M
    52. Coelho GE
    53. de Oliveira WK
    54. Tesh R
    55. Pybus OG
    56. Nunes MR
    57. Vasconcelos PF

    (2016) Zika virus in the americas: Early epidemiological and genetic findings

    Science 352:.

    https://doi.org/10.1126/science.aaf5036

    • Google Scholar
27. 1. Faye O
    2. Faye O
    3. Dupressoir A
    4. Weidmann M
    5. Ndiaye M
    6. Alpha Sall A

    (2008) One-step RT-PCR for detection of zika virus

    Journal of Clinical Virology 43:96–101.

    https://doi.org/10.1016/j.jcv.2008.05.005

    • Google Scholar
28. 1. Foy BD
    2. Kobylinski KC
    3. Foy JLC
    4. Blitvich BJ
    5. Travassos da Rosa A
    6. Haddow AD
    7. Lanciotti RS
    8. Tesh RB

    (2011a) Probable non–vector-borne transmission of zika virus, colorado, USA

    Emerging Infectious Diseases 17:880–882.

    https://doi.org/10.3201/eid1705.101939

    • Google Scholar
29. 1. Foy BD
    2. Kobylinski KC
    3. Foy JLC
    4. Blitvich BJ
    5. Travassos da Rosa A
    6. Haddow AD
    7. Lanciotti RS
    8. Tesh RB

    (2011b) Probable non–vector-borne transmission of zika virus, colorado, USA

    Emerging Infectious Diseases 17:880–882.

    https://doi.org/10.3201/eid1705.101939

    • Google Scholar
30. 1. Freifeld CC
    2. Mandl KD
    3. Reis BY
    4. Brownstein JS

    (2008) Healthmap: Global infectious disease monitoring through automated classification and visualization of internet media reports

    Journal of the American Medical Informatics Association 15:150–157.

    https://doi.org/10.1197/jamia.M2544

    • Google Scholar
31. 1. Friedman JH

    (2001) Greedy function approximation: A gradient boosting machine

    The Annals of Statistics 29:1189–1232.

    https://doi.org/10.1214/aos/1013203451

    • Google Scholar
32. 1. Fuller DO
    2. Troyo A
    3. Beier JC

    (2009) El niño southern oscillation and vegetation dynamics as predictors of dengue fever cases in costa rica

    Environmental Research Letters 4:014011.

    https://doi.org/10.1088/1748-9326/4/1/014011

    • Google Scholar
33. 1. Gaughan AE
    2. Stevens FR
    3. Linard C
    4. Jia P
    5. Tatem AJ

    (2013) High resolution population distribution maps for southeast asia in 2010 and 2015

    PloS One 8:e55882.

    https://doi.org/10.1371/journal.pone.0055882

    • Google Scholar
34. 1. Gatherer D
    2. Kohl A

    (2016) Zika virus: A previously slow pandemic spreads rapidly through the americas

    The Journal of General Virology 97:269–342.

    https://doi.org/10.1099/jgv.0.000381

    • Google Scholar
35. 1. Grard G
    2. Caron M
    3. Mombo IM
    4. Nkoghe D
    5. Mboui Ondo S
    6. Jiolle D
    7. Fontenille D
    8. Paupy C
    9. Leroy EM

    (2014) Zika virus in gabon (central africa)--2007: A new threat from aedes albopictus?

    PLoS Neglected Tropical Diseases 8:e2681.

    https://doi.org/10.1371/journal.pntd.0002681

    • Google Scholar
36. 1. Gubler DJ
    2. Clark GG

    (1995) Dengue/dengue hemorrhagic fever: The emergence of a global health problem

    Emerging Infectious Diseases 1:55–57.

    https://doi.org/10.3201/eid0102.952004

    • Google Scholar
37. 1. Haddow AD
    2. Schuh AJ
    3. Yasuda CY
    4. Kasper MR
    5. Heang V
    6. Huy R
    7. Guzman H
    8. Tesh RB
    9. Weaver SC

    (2012) Genetic characterization of zika virus strains: Geographic expansion of the asian lineage

    PLoS Neglected Tropical Diseases 6:.

    https://doi.org/10.1371/journal.pntd.0001477

    • Google Scholar
38. 1. Hales S
    2. de Wet N
    3. Maindonald J
    4. Woodward A

    (2002) Potential effect of population and climate changes on global distribution of dengue fever: An empirical model

    Lancet 360:830–834.

    https://doi.org/10.1016/S0140-6736(02)09964-6

    • Google Scholar
39. 1. Hay SI
    2. Battle KE
    3. Pigott DM
    4. Smith DL
    5. Moyes CL
    6. Bhatt S
    7. Brownstein JS
    8. Collier N
    9. Myers MF
    10. George DB
    11. Gething PW

    (2013) Global mapping of infectious disease

    Philosophical Transactions of the Royal Society B 368:20120250.

    https://doi.org/10.1098/rstb.2012.0250

    • Google Scholar
40. 1. Hay SI
    2. Tatem AJ
    3. Graham AJ
    4. Goetz SJ
    5. Rogers DJ

    (2006) Global environmental data for mapping infectious disease distribution

    Advances in Parasitology 62:37–77.

    https://doi.org/10.1016/S0065-308X(05)62002-7

    • Google Scholar
41. 1. Hennessey M
    2. Fischer M
    3. Staples JE

    (2016) Zika virus spreads to new areas - region of the americas, may 2015-january 2016

    Morb Mortal Wkly Rep 65:55–58.

    https://doi.org/10.1111/ajt.13743

    • Google Scholar
42. 1. Heymann DL
    2. Hodgson A
    3. Sall AA
    4. Freedman DO
    5. Staples JE
    6. Althabe F
    7. Baruah K
    8. Mahmud G
    9. Kandun N
    10. Vasconcelos PFC
    11. Bino S
    12. Menon KU

    (2016) Zika virus and microcephaly: Why is this situation a PHEIC?

    The Lancet 387:719–721.

    https://doi.org/10.1016/S0140-6736(16)00320-2

    • Google Scholar
43. 1. ICTV

    (2014)

    Virus taxonomy: 2014 release

    Virus taxonomy: 2014 release, 1st Edition.

    • Google Scholar
44. 1. Jones PG
    2. Thornton PK

    (2000) Marksim: Software to generate daily weather data for Latin America and Africa

    Agronomy Journal 92:445–453.

    https://doi.org/10.2134/agronj2000.923445x

    • Google Scholar
45. 1. Kraemer MU
    2. Hay SI
    3. Pigott DM
    4. Smith DL
    5. Wint GR
    6. Golding N

    (2016) Progress and challenges in infectious disease cartography

    Trends in Parasitology 32:19–29.

    https://doi.org/10.1016/j.pt.2015.09.006

    • Google Scholar
46. 1. Kraemer MUG
    2. Perkins TA
    3. Cummings DAT
    4. Zakar R
    5. Hay SI
    6. Smith DL
    7. Reiner RC

    (2015a) Big city, small world: Density, contact rates, and transmission of dengue across pakistan

    Journal of the Royal Society Interface 12:20150468.

    https://doi.org/10.1098/rsif.2015.0468

    • Google Scholar
47. 1. Kraemer MUG
    2. Sinka ME
    3. Duda KA
    4. Mylne A
    5. Shearer FM
    6. Brady OJ
    7. Messina JP
    8. Barker CM
    9. Moore CG
    10. Carvalho RG
    11. Coelho GE
    12. Van Bortel W
    13. Hendrickx G
    14. Schaffner F
    15. Wint GRW
    16. Elyazar IRF
    17. Teng H-J
    18. Hay SI

    (2015b) The global compendium of aedes aegypti and ae. albopictus occurrence

    Scientific Data 2:150035.

    https://doi.org/10.1038/sdata.2015.35

    • Google Scholar
48. 1. Kraemer MUG
    2. Sinka ME
    3. Duda KA
    4. Mylne AQN
    5. Shearer FM
    6. Barker CM
    7. Moore CG
    8. Carvalho RG
    9. Coelho GE
    10. Van Bortel W
    11. Hendrickx G
    12. Schaffner F
    13. Elyazar IRF
    14. Teng H-J
    15. Brady OJ
    16. Messina JP
    17. Pigott DM
    18. Scott TW
    19. Smith DL
    20. Wint GRW
    21. Golding N
    22. Hay SI

    (2015c) The global distribution of the arbovirus vectors aedes aegypti and ae. albopictus

    eLife 4:e08347.

    https://doi.org/10.7554/eLife.08347

    • Google Scholar
49. 1. Kutsuna S
    2. Kato Y
    3. Moi ML
    4. Kotaki A
    5. Ota M
    6. Shinohara K
    7. Kobayashi T
    8. Yamamoto K
    9. Fujiya Y
    10. Mawatari M
    11. Sato T
    12. Kunimatsu J
    13. Takeshita N
    14. Hayakawa K
    15. Kanagawa S
    16. Takasaki T
    17. Ohmagari N

    (2015) Autochthonous dengue fever, tokyo, japan, 2014

    Emerging Infectious Diseases 21:517.

    https://doi.org/10.3201/eid2103.141662

    • Google Scholar
50. 1. Lambrechts L
    2. Paaijmans KP
    3. Fansiri T
    4. Carrington LB
    5. Kramer LD
    6. Thomas MB
    7. Scott TW

    (2011) Impact of daily temperature fluctuations on dengue virus transmission by aedes aegypti

    Proceedings of the National Academy of Sciences of the United States of America 108:7460–7465.

    https://doi.org/10.1073/pnas.1101377108

    • Google Scholar
51. 1. Lanciotti RS
    2. Kosoy OL
    3. Laven JJ
    4. Velez JO
    5. Lambert AJ
    6. Johnson AJ
    7. Stanfield SM
    8. Duffy MR

    (2008) Genetic and serologic properties of zika virus associated with an epidemic, yap state, micronesia, 2007

    Emerging Infectious Diseases 14:1232–1241.

    https://doi.org/10.3201/eid1408.080287

    • Google Scholar
52. 1. Lin QH

    (2012) Enhanced vegetation index using moderate resolution imaging spectroradiometers

    In 5th International Congress on Image and Signal Processing  pp. 1043–1046.

    https://doi.org/10.1109/cisp.2012.6470008

    • Google Scholar
53. 1. Linacre ET

    (1977) Simple formula for estimating evaporation rates in various climates using temperature data alone

    Agr Meteorol 18:409–424.

    https://doi.org/10.1016/0002-1571(77)90007-3

    • Google Scholar
54. 1. Linard C
    2. Tatem AJ
    3. Gilbert M

    (2013) Modelling spatial patterns of urban growth in africa

    Applied Geography 44:23–32.

    https://doi.org/10.1016/j.apgeog.2013.07.009

    • Google Scholar
55. 1. Linard C
    2. Tatem AJ

    (2012) Large-scale spatial population databases in infectious disease research

    Int J Health Geogr 11:.

    https://doi.org/10.1186/1476-072x-11-7

    • Google Scholar
56. 1. Linthicum KJ
    2. Anyamba A
    3. Tucker CJ
    4. Kelley PW
    5. Myers MF
    6. Peters CJ

    (1999) Climate and satellite indicators to forecast rift valley fever epidemics in kenya

    Science 285:397–400.

    https://doi.org/10.1126/science.285.5426.397

    • Google Scholar
57. 1. Luz C
    2. Tai MH
    3. Santos AH
    4. Silva HH

    (2008) Impact of moisture on survival of aedes aegypti eggs and ovicidal activity of metarhizium anisopliae under laboratory conditions

    MemóRias Do Instituto Oswaldo Cruz 103:214–215.

    https://doi.org/10.1590/s0074-02762008000200016

    • Google Scholar
58. 1. Marchette NJ
    2. Garcia R
    3. Rudnick A

    (1969)

    Isolation of Zika virus from Aedes aegypti mosquitoes in Malaysia

    The American Journal of Tropical Medicine and Hygiene 18:411–415.

    • Google Scholar
59. 1. Marcondes CB
    2. Ximenes MF

    (2015) Zika virus in brazil and the danger of infestation by aedes (stegomyia) mosquitoes

    Revista Da Sociedade Brasileira De Medicina Tropical.

    https://doi.org/10.1590/0037-8682-0220-2015

    • Google Scholar
60. 1. Martines RB
    2. Bhatnagar J
    3. Keating MK
    4. Silva-Flannery L
    5. Muehlenbachs A
    6. Gary J
    7. Goldsmith C
    8. Hale G
    9. Ritter J
    10. Rollin D
    11. Shieh WJ
    12. Luz KG
    13. Ramos AM
    14. Davi HP
    15. Kleber de Oliveria W
    16. Lanciotti R
    17. Lambert A
    18. Zaki S

    (2016) Notes from the field: Evidence of zika virus infection in brain and placental tissues from two congenitally infected newborns and two fetal losses - brazil, 2015

    MMWR. Morbidity and Mortality Weekly Report 65:159–219.

    https://doi.org/10.15585/mmwr.mm6506e1

    • Google Scholar
61. 1. McCarthy M

    (2016) First US case of zika virus infection is identified in texas

    BMJ 352:i212.

    https://doi.org/10.1136/bmj.i212

    • Google Scholar
62. 1. Messina JP
    2. Brady OJ
    3. Pigott DM
    4. Brownstein JS
    5. Hoen AG
    6. Hay SI

    (2014) A global compendium of human dengue virus occurrence

    Scientific Data 1:140004.

    https://doi.org/10.1038/sdata.2014.4

    • Google Scholar
63. 1. Messina JP
    2. Pigott DM
    3. Duda KA
    4. Brownstein JS
    5. Myers MF
    6. George DB
    7. Hay SI

    (2015a) A global compendium of human crimean-congo haemorrhagic fever virus occurrence

    Scientific Data 2:150016.

    https://doi.org/10.1038/sdata.2015.16

    • Google Scholar
64. 1. Messina JP
    2. Pigott DM
    3. Golding N
    4. Duda KA
    5. Brownstein JS
    6. Weiss DJ
    7. Gibson H
    8. Robinson TP
    9. Gilbert M
    10. William Wint GR
    11. Nuttall PA
    12. Gething PW
    13. Myers MF
    14. George DB
    15. Hay SI

    (2015b) The global distribution of crimean-congo hemorrhagic fever

    Transactions of the Royal Society of Tropical Medicine and Hygiene 109:503–513.

    https://doi.org/10.1093/trstmh/trv050

    • Google Scholar
65. 1. Monlun E
    2. Zeller H
    3. Le Guenno B
    4. Traore-Lamizana M
    5. Hervy J
    6. Adam F
    7. Ferrara L
    8. Fontenille D
    9. Sylla R
    10. Mondo M

    (1992)

    Surveillance of the circulation of arbovirus of medical interest in the region of eastern Senegal

    Bulletin de la Société de pathologie exotique 86:21–28.

    • Google Scholar
66. 1. Morrison AC
    2. Zielinski-Gutierrez E
    3. Scott TW
    4. Rosenberg R

    (2008) Defining challenges and proposing solutions for control of the virus vector aedes aegypti

    PLoS Medicine 5:e68.

    https://doi.org/10.1371/journal.pmed.0050068

    • Google Scholar
67. 1. Murray CJ
    2. Barber RM
    3. Foreman KJ
    4. Abbasoglu Ozgoren A
    5. Abd-Allah F
    6. Abera SF
    7. Aboyans V
    8. Abraham JP
    9. Abubakar I
    10. Abu-Raddad LJ
    11. Abu-Rmeileh NM
    12. Achoki T
    13. Ackerman IN
    14. Ademi Z
    15. Adou AK
    16. Adsuar JC
    17. Afshin A
    18. Agardh EE
    19. Alam SS
    20. Alasfoor D
    21. Albittar MI
    22. Alegretti MA
    23. Alemu ZA
    24. Alfonso-Cristancho R
    25. Alhabib S
    26. Ali R
    27. Alla F
    28. Allebeck P
    29. Almazroa MA
    30. Alsharif U
    31. Alvarez E
    32. Alvis-Guzman N
    33. Amare AT
    34. Ameh EA
    35. Amini H
    36. Ammar W
    37. Anderson HR
    38. Anderson BO
    39. Antonio CA
    40. Anwari P
    41. Arnlöv J
    42. Arsic Arsenijevic VS
    43. Artaman A
    44. Asghar RJ
    45. Assadi R
    46. Atkins LS
    47. Avila MA
    48. Awuah B
    49. Bachman VF
    50. Badawi A
    51. Bahit MC
    52. Balakrishnan K
    53. Banerjee A
    54. Barker-Collo SL
    55. Barquera S
    56. Barregard L
    57. Barrero LH
    58. Basu A
    59. Basu S
    60. Basulaiman MO
    61. Beardsley J
    62. Bedi N
    63. Beghi E
    64. Bekele T
    65. Bell ML
    66. Benjet C
    67. Bennett DA
    68. Bensenor IM
    69. Benzian H
    70. Bernabé E
    71. Bertozzi-Villa A
    72. Beyene TJ
    73. Bhala N
    74. Bhalla A
    75. Bhutta ZA
    76. Bienhoff K
    77. Bikbov B
    78. Biryukov S
    79. Blore JD
    80. Blosser CD
    81. Blyth FM
    82. Bohensky MA
    83. Bolliger IW
    84. Bora Başara B
    85. Bornstein NM
    86. Bose D
    87. Boufous S
    88. Bourne RR
    89. Boyers LN
    90. Brainin M
    91. Brayne CE
    92. Brazinova A
    93. Breitborde NJ
    94. Brenner H
    95. Briggs AD
    96. Brooks PM
    97. Brown JC
    98. Brugha TS
    99. Buchbinder R
    100. Buckle GC
    101. Budke CM
    102. Bulchis A
    103. Bulloch AG
    104. Campos-Nonato IR
    105. Carabin H
    106. Carapetis JR
    107. Cárdenas R
    108. Carpenter DO
    109. Caso V
    110. Castañeda-Orjuela CA
    111. Castro RE
    112. Catalá-López F
    113. Cavalleri F
    114. Çavlin A
    115. Chadha VK
    116. Chang JC
    117. Charlson FJ
    118. Chen H
    119. Chen W
    120. Chiang PP
    121. Chimed-Ochir O
    122. Chowdhury R
    123. Christensen H
    124. Christophi CA
    125. Cirillo M
    126. Coates MM
    127. Coffeng LE
    128. Coggeshall MS
    129. Colistro V
    130. Colquhoun SM
    131. Cooke GS
    132. Cooper C
    133. Cooper LT
    134. Coppola LM
    135. Cortinovis M
    136. Criqui MH
    137. Crump JA
    138. Cuevas-Nasu L
    139. Danawi H
    140. Dandona L
    141. Dandona R
    142. Dansereau E
    143. Dargan PI
    144. Davey G
    145. Davis A
    146. Davitoiu DV
    147. Dayama A
    148. De Leo D
    149. Degenhardt L
    150. Del Pozo-Cruz B
    151. Dellavalle RP
    152. Deribe K
    153. Derrett S
    154. Des Jarlais DC
    155. Dessalegn M
    156. Dharmaratne SD
    157. Dherani MK
    158. Diaz-Torné C
    159. Dicker D
    160. Ding EL
    161. Dokova K
    162. Dorsey ER
    163. Driscoll TR
    164. Duan L
    165. Duber HC
    166. Ebel BE
    167. Edmond KM
    168. Elshrek YM
    169. Endres M
    170. Ermakov SP
    171. Erskine HE
    172. Eshrati B
    173. Esteghamati A
    174. Estep K
    175. Faraon EJ
    176. Farzadfar F
    177. Fay DF
    178. Feigin VL
    179. Felson DT
    180. Fereshtehnejad SM
    181. Fernandes JG
    182. Ferrari AJ
    183. Fitzmaurice C
    184. Flaxman AD
    185. Fleming TD
    186. Foigt N
    187. Forouzanfar MH
    188. Fowkes FG
    189. Paleo UF
    190. Franklin RC
    191. Fürst T
    192. Gabbe B
    193. Gaffikin L
    194. Gankpé FG
    195. Geleijnse JM
    196. Gessner BD
    197. Gething P
    198. Gibney KB
    199. Giroud M
    200. Giussani G
    201. Gomez Dantes H
    202. Gona P
    203. González-Medina D
    204. Gosselin RA
    205. Gotay CC
    206. Goto A
    207. Gouda HN
    208. Graetz N
    209. Gugnani HC
    210. Gupta R
    211. Gupta R
    212. Gutiérrez RA
    213. Haagsma J
    214. Hafezi-Nejad N
    215. Hagan H
    216. Halasa YA
    217. Hamadeh RR
    218. Hamavid H
    219. Hammami M
    220. Hancock J
    221. Hankey GJ
    222. Hansen GM
    223. Hao Y
    224. Harb HL
    225. Haro JM
    226. Havmoeller R
    227. Hay SI
    228. Hay RJ
    229. Heredia-Pi IB
    230. Heuton KR
    231. Heydarpour P
    232. Higashi H
    233. Hijar M
    234. Hoek HW
    235. Hoffman HJ
    236. Hosgood HD
    237. Hossain M
    238. Hotez PJ
    239. Hoy DG
    240. Hsairi M
    241. Hu G
    242. Huang C
    243. Huang JJ
    244. Husseini A
    245. Huynh C
    246. Iannarone ML
    247. Iburg KM
    248. Innos K
    249. Inoue M
    250. Islami F
    251. Jacobsen KH
    252. Jarvis DL
    253. Jassal SK
    254. Jee SH
    255. Jeemon P
    256. Jensen PN
    257. Jha V
    258. Jiang G
    259. Jiang Y
    260. Jonas JB
    261. Juel K
    262. Kan H
    263. Karch A
    264. Karema CK
    265. Karimkhani C
    266. Karthikeyan G
    267. Kassebaum NJ
    268. Kaul A
    269. Kawakami N
    270. Kazanjan K
    271. Kemp AH
    272. Kengne AP
    273. Keren A
    274. Khader YS
    275. Khalifa SE
    276. Khan EA
    277. Khan G
    278. Khang YH
    279. Kieling C
    280. Kim D
    281. Kim S
    282. Kim Y
    283. Kinfu Y
    284. Kinge JM
    285. Kivipelto M
    286. Knibbs LD
    287. Knudsen AK
    288. Kokubo Y
    289. Kosen S
    290. Krishnaswami S
    291. Kuate Defo B
    292. Kucuk Bicer B
    293. Kuipers EJ
    294. Kulkarni C
    295. Kulkarni VS
    296. Kumar GA
    297. Kyu HH
    298. Lai T
    299. Lalloo R
    300. Lallukka T
    301. Lam H
    302. Lan Q
    303. Lansingh VC
    304. Larsson A
    305. Lawrynowicz AE
    306. Leasher JL
    307. Leigh J
    308. Leung R
    309. Levitz CE
    310. Li B
    311. Li Y
    312. Li Y
    313. Lim SS
    314. Lind M
    315. Lipshultz SE
    316. Liu S
    317. Liu Y
    318. Lloyd BK
    319. Lofgren KT
    320. Logroscino G
    321. Looker KJ
    322. Lortet-Tieulent J
    323. Lotufo PA
    324. Lozano R
    325. Lucas RM
    326. Lunevicius R
    327. Lyons RA
    328. Ma S
    329. Macintyre MF
    330. Mackay MT
    331. Majdan M
    332. Malekzadeh R
    333. Marcenes W
    334. Margolis DJ
    335. Margono C
    336. Marzan MB
    337. Masci JR
    338. Mashal MT
    339. Matzopoulos R
    340. Mayosi BM
    341. Mazorodze TT
    342. Mcgill NW
    343. Mcgrath JJ
    344. Mckee M
    345. Mclain A
    346. Meaney PA
    347. Medina C
    348. Mehndiratta MM
    349. Mekonnen W
    350. Melaku YA
    351. Meltzer M
    352. Memish ZA
    353. Mensah GA
    354. Meretoja A
    355. Mhimbira FA
    356. Micha R
    357. Miller TR
    358. Mills EJ
    359. Mitchell PB
    360. Mock CN
    361. Mohamed Ibrahim N
    362. Mohammad KA
    363. Mokdad AH
    364. Mola GL
    365. Monasta L
    366. Montañez Hernandez JC
    367. Montico M
    368. Montine TJ
    369. Mooney MD
    370. Moore AR
    371. Moradi-Lakeh M
    372. Moran AE
    373. Mori R
    374. Moschandreas J
    375. Moturi WN
    376. Moyer ML
    377. Mozaffarian D
    378. Msemburi WT
    379. Mueller UO
    380. Mukaigawara M
    381. Mullany EC
    382. Murdoch ME
    383. Murray J
    384. Murthy KS
    385. Naghavi M
    386. Naheed A
    387. Naidoo KS
    388. Naldi L
    389. Nand D
    390. Nangia V
    391. Narayan KM
    392. Nejjari C
    393. Neupane SP
    394. Newton CR
    395. Ng M
    396. Ngalesoni FN
    397. Nguyen G
    398. Nisar MI
    399. Nolte S
    400. Norheim OF
    401. Norman RE
    402. Norrving B
    403. Nyakarahuka L
    404. Oh IH
    405. Ohkubo T
    406. Ohno SL
    407. Olusanya BO
    408. Opio JN
    409. Ortblad K
    410. Ortiz A
    411. Pain AW
    412. Pandian JD
    413. Panelo CI
    414. Papachristou C
    415. Park EK
    416. Park JH
    417. Patten SB
    418. Patton GC
    419. Paul VK
    420. Pavlin BI
    421. Pearce N
    422. Pereira DM
    423. Perez-Padilla R
    424. Perez-Ruiz F
    425. Perico N
    426. Pervaiz A
    427. Pesudovs K
    428. Peterson CB
    429. Petzold M
    430. Phillips MR
    431. Phillips BK
    432. Phillips DE
    433. Piel FB
    434. Plass D
    435. Poenaru D
    436. Polinder S
    437. Pope D
    438. Popova S
    439. Poulton RG
    440. Pourmalek F
    441. Prabhakaran D
    442. Prasad NM
    443. Pullan RL
    444. Qato DM
    445. Quistberg DA
    446. Rafay A
    447. Rahimi K
    448. Rahman SU
    449. Raju M
    450. Rana SM
    451. Razavi H
    452. Reddy KS
    453. Refaat A
    454. Remuzzi G
    455. Resnikoff S
    456. Ribeiro AL
    457. Richardson L
    458. Richardus JH
    459. Roberts DA
    460. Rojas-Rueda D
    461. Ronfani L
    462. Roth GA
    463. Rothenbacher D
    464. Rothstein DH
    465. Rowley JT
    466. Roy N
    467. Ruhago GM
    468. Saeedi MY
    469. Saha S
    470. Sahraian MA
    471. Sampson UK
    472. Sanabria JR
    473. Sandar L
    474. Santos IS
    475. Satpathy M
    476. Sawhney M
    477. Scarborough P
    478. Schneider IJ
    479. Schöttker B
    480. Schumacher AE
    481. Schwebel DC
    482. Scott JG
    483. Seedat S
    484. Sepanlou SG
    485. Serina PT
    486. Servan-Mori EE
    487. Shackelford KA
    488. Shaheen A
    489. Shahraz S
    490. Shamah Levy T
    491. Shangguan S
    492. She J
    493. Sheikhbahaei S
    494. Shi P
    495. Shibuya K
    496. Shinohara Y
    497. Shiri R
    498. Shishani K
    499. Shiue I
    500. Shrime MG
    501. Sigfusdottir ID
    502. Silberberg DH
    503. Simard EP
    504. Sindi S
    505. Singh A
    506. Singh JA
    507. Singh L
    508. Skirbekk V
    509. Slepak EL
    510. Sliwa K
    511. Soneji S
    512. Søreide K
    513. Soshnikov S
    514. Sposato LA
    515. Sreeramareddy CT
    516. Stanaway JD
    517. Stathopoulou V
    518. Stein DJ
    519. Stein MB
    520. Steiner C
    521. Steiner TJ
    522. Stevens A
    523. Stewart A
    524. Stovner LJ
    525. Stroumpoulis K
    526. Sunguya BF
    527. Swaminathan S
    528. Swaroop M
    529. Sykes BL
    530. Tabb KM
    531. Takahashi K
    532. Tandon N
    533. Tanne D
    534. Tanner M
    535. Tavakkoli M
    536. Taylor HR
    537. Te Ao BJ
    538. Tediosi F
    539. Temesgen AM
    540. Templin T
    541. Ten Have M
    542. Tenkorang EY
    543. Terkawi AS
    544. Thomson B
    545. Thorne-Lyman AL
    546. Thrift AG
    547. Thurston GD
    548. Tillmann T
    549. Tonelli M
    550. Topouzis F
    551. Toyoshima H
    552. Traebert J
    553. Tran BX
    554. Trillini M
    555. Truelsen T
    556. Tsilimbaris M
    557. Tuzcu EM
    558. Uchendu US
    559. Ukwaja KN
    560. Undurraga EA
    561. Uzun SB
    562. Van Brakel WH
    563. Van De Vijver S
    564. van Gool CH
    565. Van Os J
    566. Vasankari TJ
    567. Venketasubramanian N
    568. Violante FS
    569. Vlassov VV
    570. Vollset SE
    571. Wagner GR
    572. Wagner J
    573. Waller SG
    574. Wan X
    575. Wang H
    576. Wang J
    577. Wang L
    578. Warouw TS
    579. Weichenthal S
    580. Weiderpass E
    581. Weintraub RG
    582. Wenzhi W
    583. Werdecker A
    584. Westerman R
    585. Whiteford HA
    586. Wilkinson JD
    587. Williams TN
    588. Wolfe CD
    589. Wolock TM
    590. Woolf AD
    591. Wulf S
    592. Wurtz B
    593. Xu G
    594. Yan LL
    595. Yano Y
    596. Ye P
    597. Yentür GK
    598. Yip P
    599. Yonemoto N
    600. Yoon SJ
    601. Younis MZ
    602. Yu C
    603. Zaki ME
    604. Zhao Y
    605. Zheng Y
    606. Zonies D
    607. Zou X
    608. Salomon JA
    609. Lopez AD
    610. Vos T
    611. GBD 2013 DALYs and HALE Collaborators

    (2015) Global, regional, and national disability-adjusted life years (dalys) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: Quantifying the epidemiological transition

    Lancet  386:2145–2191.

    https://doi.org/10.1016/S0140-6736(15)61340-X

    • Google Scholar
68. 1. Murray NE
    2. Quam MB
    3. Wilder-Smith A

    (2013) Epidemiology of dengue: Past, present and future prospects

    Clinical Epidemiology 5:299–309.

    https://doi.org/10.2147/CLEP.S34440

    • Google Scholar
69. 1. Musso D
    2. Nilles EJ
    3. Cao-Lormeau V-M

    (2014) Rapid spread of emerging zika virus in the pacific area

    Clinical Microbiology and Infection 20:O595–O596.

    https://doi.org/10.1111/1469-0691.12707

    • Google Scholar
70. 1. Musso D
    2. Cao-Lormeau VM
    3. Gubler DJ

    (2015a) Zika virus: Following the path of dengue and chikungunya?

    Lancet 386:243–247.

    https://doi.org/10.1016/S0140-6736(15)61273-9

    • Google Scholar
71. 1. Musso D
    2. Roche C
    3. Robin E
    4. Nhan T
    5. Teissier A
    6. Cao-Lormeau VM

    (2015b) Potential sexual transmission of zika virus

    Emerging Infectious Diseases 21:359–361.

    https://doi.org/10.3201/eid2102.141363

    • Google Scholar
72. 1. Mylne AQ
    2. Pigott DM
    3. Longbottom J
    4. Shearer F
    5. Duda KA
    6. Messina JP
    7. Weiss DJ
    8. Moyes CL
    9. Golding N
    10. Hay SI

    (2015) Mapping the zoonotic niche of lassa fever in africa

    Transactions of the Royal Society of Tropical Medicine and Hygiene 109:483–492.

    https://doi.org/10.1093/trstmh/trv047

    • Google Scholar
73. Book

    1. Nelson A

    (2008)

    Travel Time to Major Cities: A Global Map of Accessibility

    Ispra, Italy: GEMU-JRCOTE Commission.

    • Google Scholar
74. 1. Nunes MR
    2. Faria NR
    3. de Vasconcelos JM
    4. Golding N
    5. Kraemer MU
    6. de Oliveira LF
    7. Azevedo RS
    8. da Silva DE
    9. da Silva EV
    10. da Silva SP
    11. Carvalho VL
    12. Coelho GE
    13. Cruz AC
    14. Rodrigues SG
    15. Vianez JL
    16. Nunes BT
    17. Cardoso JF
    18. Tesh RB
    19. Hay SI
    20. Pybus OG
    21. Vasconcelos PF

    (2015) Emergence and potential for spread of chikungunya virus in brazil

    BMC Medicine 13:.

    https://doi.org/10.1186/s12916-015-0348-x

    • Google Scholar
75. 1. Patiño-Barbosa AM
    2. Medina I
    3. Gil-Restrepo AF
    4. Rodriguez-Morales AJ

    (2015) Zika: Another sexually transmitted infection?

    Sexually Transmitted Infections 91:.

    https://doi.org/10.1136/sextrans-2015-052189

    • Google Scholar
76. 1. Phillips SJ
    2. Dudík M
    3. Elith J
    4. Graham CH
    5. Lehmann A
    6. Leathwick J
    7. Ferrier S

    (2009) Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data

    Ecological Applications 19:181–197.

    https://doi.org/10.1890/07-2153.1

    • Google Scholar
77. 1. Pigott DM
    2. Bhatt S
    3. Golding N
    4. Duda KA
    5. Battle KE
    6. Brady OJ
    7. Messina JP
    8. Balard Y
    9. Bastien P
    10. Pratlong F
    11. Brownstein JS
    12. Freifeld CC
    13. Mekaru SR
    14. Gething PW
    15. George DB
    16. Myers MF
    17. Reithinger R
    18. Hay SI

    (2014b) Global distribution maps of the leishmaniases

    eLife 3:.

    https://doi.org/10.7554/eLife.02851

    • Google Scholar
78. 1. Pigott DM
    2. Bhatt S
    3. Golding N
    4. Duda KA
    5. Battle KE
    6. Brady OJ
    7. Messina JP
    8. Balard Y
    9. Bastien P
    10. Pratlong F
    11. Brownstein JS
    12. Freifeld CC
    13. Mekaru SR
    14. Gething PW
    15. George DB
    16. Myers MF
    17. Reithinger R
    18. Hay SI

    (2014) Global distribution maps of the leishmaniases

    eLife 3:.

    https://doi.org/10.7554/eLife.02851

    • Google Scholar
79. 1. Pigott DM
    2. Golding N
    3. Mylne A
    4. Huang Z
    5. Weiss DJ
    6. Brady OJ
    7. Kraemer MU
    8. Hay SI

    (2015a) Mapping the zoonotic niche of marburg virus disease in africa

    Transactions of the Royal Society of Tropical Medicine and Hygiene 109:366–378.

    https://doi.org/10.1093/trstmh/trv024

    • Google Scholar
80. 1. Pigott DM
    2. Howes RE
    3. Wiebe A
    4. Battle KE
    5. Golding N
    6. Gething PW
    7. Dowell SF
    8. Farag TH
    9. Garcia AJ
    10. Kimball AM
    11. Krause LK
    12. Smith CH
    13. Brooker SJ
    14. Kyu HH
    15. Vos T
    16. Murray CJL
    17. Moyes CL
    18. Hay SI

    (2015b) Prioritising infectious disease mapping

    PLOS Neglected Tropical Diseases 9:e0003756.

    https://doi.org/10.1371/journal.pntd.0003756

    • Google Scholar
81. 1. Pinto E
    2. Coelho M
    3. Oliver L
    4. Massad E

    (2011) The influence of climate variables on dengue in singapore

    International Journal of Environmental Health Research 21:415–426.

    https://doi.org/10.1080/09603123.2011.572279

    • Google Scholar
82. 1. Pond WL

    (1963) ARTHROPOD-borne virus antibodies in sera from residents of south-east asia

    Transactions of the Royal Society of Tropical Medicine and Hygiene 57:364–371.

    https://doi.org/10.1016/0035-9203(63)90100-7

    • Google Scholar
83. 1. Powell JR
    2. Tabachnick WJ

    (2013) History of domestication and spread of aedes aegypti--a review

    MemóRias Do Instituto Oswaldo Cruz 108 Suppl 1:11–17.

    https://doi.org/10.1590/0074-0276130395

    • Google Scholar
84. 1. Reiskind MH
    2. Lounibos LP

    (2009) Effects of intraspecific larval competition on adult longevity in the mosquitoes aedes aegypti and aedes albopictus

    Medical and Veterinary Entomology 23:62–68.

    https://doi.org/10.1111/j.1365-2915.2008.00782.x

    • Google Scholar
85. 1. Restrepo AC
    2. Baker P
    3. Clements AC

    (2014) National spatial and temporal patterns of notified dengue cases, colombia 2007-2010

    Tropical Medicine & International Health 19:863–871.

    https://doi.org/10.1111/tmi.12325

    • Google Scholar
86. 1. Roiz D
    2. Boussès P
    3. Simard F
    4. Paupy C
    5. Fontenille D

    (2015) Autochthonous chikungunya transmission and extreme climate events in southern france

    PLoS Neglected Tropical Diseases 9:e0003854.

    https://doi.org/10.1371/journal.pntd.0003854

    • Google Scholar
87. 1. Russell BM
    2. Kay BH
    3. Shipton W

    (2001) Survival of aedes aegypti (diptera: Culicidae) eggs in surface and subterranean breeding sites during the northern queensland dry season

    Journal of Medical Entomology 38:441–445.

    https://doi.org/10.1603/0022-2585-38.3.441

    • Google Scholar
88. 1. Salje H
    2. Cauchemez S
    3. Alera MT
    4. Rodriguez-Barraquer I
    5. Thaisomboonsuk B
    6. Srikiatkhachorn A
    7. Lago CB
    8. Villa D
    9. Klungthong C
    10. Tac-An IA
    11. Fernandez S
    12. Velasco JM
    13. Roque VG
    14. Nisalak A
    15. Macareo LR
    16. Levy JW
    17. Cummings D
    18. Yoon IK

    (2016) Reconstruction of 60 years of chikungunya epidemiology in the philippines demonstrates episodic and focal transmission

    The Journal of Infectious Diseases 213:604–610.

    https://doi.org/10.1093/infdis/jiv470

    • Google Scholar
89. 1. Sang S
    2. Yin W
    3. Bi P
    4. Zhang H
    5. Wang C
    6. Liu X
    7. Chen B
    8. Yang W
    9. Liu Q

    (2014) Predicting local dengue transmission in guangzhou, china, through the influence of imported cases, mosquito density and climate variability

    PloS One 9:e102755.

    https://doi.org/10.1371/journal.pone.0102755

    • Google Scholar
90. 1. Sankari T
    2. Hoti SL
    3. Singh TB
    4. Shanmugavel J

    (2012)

    Outbreak of dengue virus serotype-2 (DENV-2) of Cambodian origin in Manipur, India - association with meteorological factors

    The Indian Journal of Medical Research 136:649–655.

    • Google Scholar
91. 1. Schneider A
    2. Friedl MA
    3. Potere D

    (2009) A new map of global urban extent from MODIS satellite data

    Environmental Research Letters 4:044003.

    https://doi.org/10.1088/1748-9326/4/4/044003

    • Google Scholar
92. 1. Schneider A
    2. Friedl MA
    3. Potere D

    (2010) Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’

    Remote Sensing of Environment 114:1733–1746.

    https://doi.org/10.1016/j.rse.2010.03.003

    • Google Scholar
93. 1. Schneider A
    2. Mertes CM
    3. Tatem AJ
    4. Tan B
    5. Sulla-Menashe D
    6. Graves SJ
    7. Patel NN
    8. Horton JA
    9. Gaughan AE
    10. Rollo JT
    11. Schelly IH
    12. Stevens FR
    13. Dastur A

    (2015) A new urban landscape in east–southeast asia, 2000–2010

    Environmental Research Letters 10:034002.

    https://doi.org/10.1088/1748-9326/10/3/034002

    • Google Scholar
94. 1. Scott TW
    2. Takken W

    (2012) Feeding strategies of anthropophilic mosquitoes result in increased risk of pathogen transmission

    Trends in Parasitology 28:114–121.

    https://doi.org/10.1016/j.pt.2012.01.001

    • Google Scholar
95. 1. Socio-economic Data and Applications Center

    (2015)

    Gridded Population of the World, v4 (GPWv4)

    Gridded Population of the World, v4 (GPWv4).

    • Google Scholar
96. 1. Smithburn K

    (1954a)

    Neutralizing antibodies against arthropod-borne viruses in the sera of long-time residents of Malaya and Borneo

    American Journal of Hygiene 59:157–163.

    • Google Scholar
97. 1. Smithburn KC
    2. Taylor RM
    3. Rizk F
    4. Kader A

    (1954b)

    Immunity to certain arthropod-borne viruses among indigenous residents of Egypt

    The American Journal of Tropical Medicine and Hygiene 3:9–18.

    • Google Scholar
98. 1. Sorichetta A
    2. Hornby GM
    3. Stevens FR
    4. Gaughan AE
    5. Linard C
    6. Tatem AJ

    (2015) High-resolution gridded population datasets for latin america and the caribbean in 2010, 2015, and 2020

    Scientific Data 2:.

    https://doi.org/10.1038/sdata.2015.45

    • Google Scholar
99. 1. Sota T
    2. Mogi M

    (1992) Interspecific variation in desiccation survival time of aedes (stegomyia) mosquito eggs is correlated with habitat and egg size

    Oecologia 90:353–358.

    https://doi.org/10.1007/BF00317691

    • Google Scholar
100. 1. Stevens FR
     2. Gaughan AE
     3. Linard C
     4. Tatem AJ

     (2015) Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data

     PloS One 10:e0107042.

     https://doi.org/10.1371/journal.pone.0107042

     • Google Scholar
101. 1. Stoddard ST
     2. Forshey BM
     3. Morrison AC
     4. Paz-Soldan VA
     5. Vazquez-Prokopec GM
     6. Astete H
     7. Reiner RC
     8. Vilcarromero S
     9. Elder JP
     10. Halsey ES
     11. Kochel TJ
     12. Kitron U
     13. Scott TW

     (2013) House-to-house human movement drives dengue virus transmission

     Proceedings of the National Academy of Sciences of the United States of America 110:994–999.

     https://doi.org/10.1073/pnas.1213349110

     • Google Scholar
102. 1. Tatem AJ
     2. Campbell J
     3. Guerra-Arias M
     4. de Bernis L
     5. Moran A
     6. Matthews Z

     (2014) Mapping for maternal and newborn health: The distributions of women of childbearing age, pregnancies and births

     International Journal of Health Geographics 13:.

     https://doi.org/10.1186/1476-072X-13-2

     • Google Scholar
103. 1. Thu HM
     2. Aye KM
     3. Thein S

     (1998)

     The effect of temperature and humidity on dengue virus propagation in Aedes aegypti mosquitos

     The Southeast Asian Journal of Tropical Medicine and Public Health 29:280–284.

     • Google Scholar
104. 1. Troyo A
     2. Fuller DO
     3. Calderón-Arguedas O
     4. Solano ME
     5. Beier JC

     (2009) Urban structure and dengue fever in puntarenas, costa rica

     Singapore Journal of Tropical Geography 30:265–282.

     https://doi.org/10.1111/j.1467-9493.2009.00367.x

     • Google Scholar
105. 1. Trpis M

     (1972)

     Dry season survival of Aedes aegypti eggs in various breeding sites in the Dar es Salaam area, Tanzania

     Bulletin of the World Health Organization 47:.

     • Google Scholar
106. Book

     1. United Nations

     (2014)

     World Urbanization Prospects: The 2014 Revision

     New York.

     • Google Scholar
107. Book

     1. UNFPA

     (2014)

     The State of the World’s Midwifery 2014: A Universal Pathway. A Woman's Right to Health

     UNFPA.

     • Google Scholar
108. 1. Wan ZM
     2. Zhang YL
     3. Zhan QC
     4. Li ZL

     (2002) The MODIS land-surface temperature products for regional environmental monitoring and global change studies

     Int Geosci Remote Se pp. 3683–3685.

     https://doi.org/10.1109/igarss.2002.1027290

     • Google Scholar
109. 1. Weaver SC
     2. Reisen WK

     (2010) Present and future arboviral threats

     Antiviral Research 85:328–345.

     https://doi.org/10.1016/j.antiviral.2009.10.008

     • Google Scholar
110. 1. Weiss DJ
     2. Atkinson PM
     3. Bhatt S
     4. Mappin B
     5. Hay SI
     6. Gething PW

     (2014) An effective approach for gap-filling continental scale remotely sensed time-series

     ISPRS Journal of Photogrammetry and Remote Sensing 98:106–118.

     https://doi.org/10.1016/j.isprsjprs.2014.10.001

     • Google Scholar
111. 1. Wenger SJ
     2. Olden JD

     (2012) Assessing transferability of ecological models: An underappreciated aspect of statistical validation

     Methods in Ecology and Evolution 3:260–267.

     https://doi.org/10.1111/j.2041-210X.2011.00170.x

     • Google Scholar
112. 1. WHO Collaborating Center for Reference and Research on Arboviruses and Hemorrhagic Fever Viruses: Annual Report

     (1999)

     143, Dakar, Senegal

     143, Dakar, Senegal.

     • Google Scholar
113. 1. Woolhouse M
     2. Scott F
     3. Hudson Z
     4. Howey R
     5. Chase-Topping M

     (2012) Human viruses: Discovery and emergence

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 367:2864–2871.

     https://doi.org/10.1098/rstb.2011.0354

     • Google Scholar
114. Website

     1. World Health Organisation

     (2015) Zika virus infection – Brazil and Colombia.

     https://cdn.elifesciences.org/articles/15272/ http://www.who.int/csr/don/21-october-2015-zika/en/
115. 1. WorldPop

     (2015)

     High Resolution Age-Structured Population Distribution Maps. U. O. S. GeoData Institute

     High Resolution Age-Structured Population Distribution Maps. U. O. S. GeoData Institute.

     • Google Scholar
116. 1. Zanluca C
     2. Melo VC
     3. Mosimann AL
     4. Santos GI
     5. Santos CN
     6. Luz K

     (2015) First report of autochthonous transmission of zika virus in brazil

     MemóRias Do Instituto Oswaldo Cruz 110:569–572.

     https://doi.org/10.1590/0074-02760150192

     • Google Scholar

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

• 36,835

  views

• 8,418

  downloads

• 286

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.