Mosquitoes spread many disease-causing viruses and parasites between people and other animals, including viral infections such as dengue and chikungunya. Both infections cause high fevers often accompanied with excruciating joint pain or other flu-like symptoms. Dengue and chikungunya have become growing public health problems over the last fifty years. Today about half of the world's population is at risk of dengue infection, while chikungunya outbreaks, which were previously limited to Africa and Asia, have recently been reported in the Caribbean, South America and Europe.

The dengue and chikungunya viruses are transmitted between people by two species of mosquitoes called Aedes aegypti and Ae. albopictus. Therefore it is important to work out where these mosquito species are found around the globe to identify the areas at risk. It is also important to predict where these species could become established if they were introduced, in order to identify areas that could become at risk in the future.

Kraemer et al. now provide updated predictions about the distribution of these two mosquito species around the globe. These predictions are based upon the most up-to-date data on the known locations of the species combined with information on environmental conditions across the globe. The updated maps show that these Aedes mosquitoes are now found across all continents, including North America and Europe.

Aedes albopictus mosquitoes in particular are rapidly expanding their territory around the globe. Kraemer et al. used their new maps to show that, unlike in the United States, many of the areas in Europe and China that could support this mosquito species do not yet appear to have been colonized.

These findings provide a map of the distribution of both species as it stands at the moment. Further work is now needed to better understand which factors are contributing to the rapid expansion of these mosquitoes' range and what might be done to control this spread.

The mosquitoes Aedes aegypti [= Stegomyia aegypti] and Aedes albopictus [= Stegomyia albopicta] (Reinert et al., 2009) are vectors of several globally important arboviruses, including dengue virus (DENV) (Simmons et al., 2012), yellow fever virus (Jentes et al., 2011), and chikungunya virus (CHIKV) (Leparc-Goffart et al., 2014). The public health impact of DENV and CHIKV has increased dramatically over the last 50 years, with both diseases spreading to new geographic locations and increasing in incidence within their range (Weaver, 2014). The remaining burden of vaccine-preventable yellow fever is similarly likely to be dramatically underestimated (Garske et al., 2014). DENV, with a nearly ubiquitous distribution in the tropics and more recently introduced to Europe (ECDC, 2014; Schaffner and Mathis, 2014), is the most prevalent human arboviral infection causing 100 million apparent annual infections world-wide with almost half of the world's population at risk of infection (Brady et al., 2012; Bhatt et al., 2013). CHIKV recently received considerable public health attention due to the outbreaks in Réunion in 2005–2006 (225,000 infections) (Borgherini et al., 2007), Italy in 2007 (205 infections) (Rezza et al., 2007), and France in 2010 and 2014 (2 and 11 locally transmitted cases, respectively) (La Ruche et al., 2010; Grandadam et al., 2011; Paty et al., 2014) as well as its recent invasion into the Americas with over 1 million cases recorded to date (Cauchemez et al., 2014; Johansson et al., 2014; Morens and Fauci, 2014). Increases in distribution and intensity of transmission are compounded by the lack of commercially available antivirals or vaccines for either disease (Simmons et al., 2012; Roy et al., 2014), although new therapeutics and vaccines are in development (McArthur et al., 2013; Powers, 2014; Villar et al., 2015). Similarly, while yellow fever infections have been on the decline due to extensive vector control and an effective vaccine developed more than 70 years ago, it still causes a significant disease burden in Africa and South America (Poland et al., 1981; World Health Organization, 1990; Garske et al., 2014). Given the public health impact of these diseases and their rapid global spread, understanding the current and future distribution, and determining the geographic limits of transmission and transmission intensity, will enable more efficient planning for disease control (Carrington and Simmons, 2014; Semenza et al., 2014; Messina et al., 2015). Because these diseases can only persist where their mosquito vectors, Ae. aegypti and Ae. albopictus are present, understanding the distributions of these two species underpins this strategy.

The global expansion of these arboviruses was preceded by the global spread of their vectors (Charrel et al., 2014). Ae. aegypti originated in Africa where its ancestral form was a zoophilic treehole mosquito named Ae. aegypti formosus (Brown et al., 2014). The domestic form Ae. aegypti is genetically distinct with discrete geographic niches (Brown et al., 2011). It was hypothesised that due to harsh conditions coupled with the onset of the slave trade, Ae. aegypti were introduced into the New World from Africa, from where it subsequently spread globally to tropical and sub-tropical regions of the world (Brown et al., 2014). Ae. albopictus, originally a zoophilic forest species from Asia, spread to islands in the Indian and Pacific Oceans (Delatte et al., 2009). During the 1980s it rapidly expanded its range to Europe, the United States and Brazil (Medlock et al., 2012; Carvalho et al., 2014). Today both Ae. aegypti and Ae. albopictus are present in most Asian cities and large parts of the Americas (Lambrechts et al., 2011). Ae. aegypti feed almost exclusively on humans in daylight hours and typically rest indoors (Scott and Takken, 2012). In contrast Ae. albopictus is usually exophagic and bites humans and animals opportunistically (Paupy et al., 2009) but has also been shown to exhibit strongly anthropophilic behavior similar to Ae. aegypti in specific contexts (Ponlawat and Harrington, 2005; Delatte et al., 2010).

A number of previous studies have mapped the global or regional distributions of Ae. aegypti and Ae. albopictus by focusing on different aspects of their ecology. The majority examined the impacts of climatic conditions, often with an exclusive focus on temperature. Kobayashi et al. (2002) and Nawrocki and Hawley (1987) used results from laboratory studies to identify potential limits of establishment in Japan and Asia suggesting a minimum mean temperature in the coldest months of −2°C and −5°C respectively limits their distribution. Brady et al. (2013) extended that work by modeling the adult survival of both species under laboratory and field conditions, indicating that Ae. albopictus has higher survival rates than Ae. aegypti, though adults of the latter can tolerate a wider range of temperatures. Applying these results to global temperature data, Brady et al. (2014) produced maps indicating areas where the temperature is suitable for these vectors to persist. Whilst temperature is clearly a crucial factor constraining the distribution of the two species, these results alone are not sufficient to discriminate between areas where the species can and cannot persist. Other studies went further using statistical models, predicting the distributions of both species (though particularly Ae. albopictus) using a broader range of climatic variables including precipitation (Benedict et al., 2007; Medley, 2010; Fischer et al., 2011; Caminade et al., 2012; Khormi and Kumar 2014; Campbell et al., 2015).

Whilst these studies incorporated several generic climatic factors to predict the current and future distribution of the species, we were able to integrate a bespoke species-specific temperature suitability covariate and account for anthropogenic factors that are known to influence Ae. aegypti and Ae. albopictus distributions (Reiter et al., 2003). Both species are container-inhabiting but differ in their behaviour and biology so that they occupy different niches (Eisen and Moore, 2013). A few local studies showed, however, that local spread of Ae. albopictus and declining Ae. aegypti populations might be linked to inter-species competition (O'Meara et al., 1995; Daugherty et al., 2000; Juliano et al., 2007) and/or non-reciprocal cross-species inseminations (Bargielowski et al., 2013). Socio-economic factors affecting the distribution of the Aedes mosquitoes other than the use of containers to store water, include the use of air-conditioning, housing quality, and the rate of urbanisation (Ramos et al., 2008; Aström et al., 2012). In addition to exclusively focusing on meteorological factors in determining the spatial extent of the Aedes mosquitoes, many models used small sets of input occurrence data, which were biased towards particular countries with well-developed surveillance systems, such as, Brazil and Taiwan (Benedict et al., 2007; Medley, 2010; Fischer et al., 2011; Campbell et al., 2015).

In this context, we set out to model the global distribution of these two important vector species, compiling the most comprehensive occurrence dataset to date from published literature and national entomological surveys. To overcome previous modelling limitations, a probabilistic species distribution model using Boosted Regression Trees (BRT) was produced for each vector. Our models combine environmental and, for the first time, land-cover variables to predict the global distribution of both species at high spatial resolution. Importantly, the models quantify prediction uncertainty and aim at identifying key contributing factors and inter-species differences in their environmental niches.

In total, data collection yielded 19,930 and 22,137 spatially unique occurrence records for Ae. aegypti and Ae. albopictus respectively, which were used to train the distribution models. This includes up-to date records from national entomological surveys from Brazil and Taiwan for both species (Carvalho et al., 2014; Yang et al., 2014). For Ae. aegypti, >60% of all occurrence records are from Asia and Oceania, 35% are from the Americas and only 575 unique occurrences are available for Africa and Europe (Table 1a). Similarly for Ae. albopictus, most of the occurrences are from Asia (75%), 23% are from the Americas and only 542 records are available from Europe and Africa (Table 1b). For each continent the top 10 countries in terms of occurrences recorded are shown for both species (Table 1). The geographic distribution of the occurrence records is the widest ever recorded with particularly high spatial and temporal resolution in Taiwan and Brazil for both species and in the United States for Ae. albopictus. All occurrence data have been made openly available through an online data repository to ensure consistency and reproducibility (Pigott and Kraemer, 2014; Kraemer et al., 2015a).

Maps showing the predicted global distribution for Ae. aegypti and Ae. albopictus are presented in Figures 1, 2, respectively. The distributions of the two species differ markedly in a number of places. Ae. aegypti is predicted to occur primarily in the tropics and sub-tropics, with concentrations in northern Brazil and southeast Asia including all of India, but with relatively few areas of suitability in Europe (only Spain and Greece) and temperate North America. In Australia, however, Ae. aegypti shows a wider geographic distribution than Ae. albopictus, which is confined to the east coast, largely reflecting the known historic distribution of Ae. aegypti. By contrast, the distribution of Ae. albopictus extends into southern Europe (Figure 3A), northern China, southern Brazil, northern United States (3b), and Japan. Again, this reflects the current and historic distribution of Ae. albopictus and the ability of the species to tolerate lower temperatures (Tsuda and Takagi, 2001; Lounibos et al., 2002; Thomas et al., 2012; Brady et al., 2014).

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Figure 2

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In Europe, the predicted potential distribution of Ae. albopictus contains most of the known occurrence points, but suitability is also predicted in Portugal and the west of Spain, and in much of south-eastern Europe and the Balkans, where the species has yet to be reported. Similarly, in China Ae. albopictus has yet to be reported from much of the area predicted to be environmentally suitable. By contrast, in the United States the species has been reported from almost all of the predicted suitable areas, with the exception of a small band of predicted suitability on the western slope of the Sierra Nevada. Due to the relatively sparse reporting from Africa it remains uncertain whether areas predicted to be highly suitable are already infested or have yet to be colonized by the species. Ae. albopictus for example has only been reported from some West African countries (Nigeria, Cameroon, Gabon, the Central African Republic, Congo, Côte d'Ivoire) and Madagascar, and South Africa (as well as some islands in the Indian Ocean). The distribution of Ae. aegypti in Africa seems to be much wider, with reports of species occurrence in over 30 countries.

For both species, the most important predictor was temperature. Temperature suitability indices had high relative influence statistics for both species; this variable was selected in approximately half of regression tree decisions for Ae. aegypti (54.9%, CI = 53.7–56%) and Ae. albopictus (44.3%, CI = 42.7–45.6%). The full definition of a relative influence statistic is given in the ‘Materials and methods’ section under the heading Predictive performance and relative influence of covariates. Precipitation and vegetation indices made up the remainder of predictors. Urban land cover made very little contribution to either model (Table 2). Model evaluation statistics under cross-validation were high (AUC: 0.87 and 0.9 respectively) for both model ensembles, indicating high predictive performance of the model. Effect plots for each covariate are shown in Figure 1—figure supplement 2. Maps of uncertainty associated with these predictions are presented in Figure 1—figure supplement 3.

By combining the most comprehensive dataset of occurrence records with an advanced modelling approach and a bespoke set of environmental and land-cover correlates, we have produced contemporary high-resolution probability of occurrence maps for Ae. aegypti and Ae. albopictus, two of the most important disease vectors globally. Dengue and chikungunya, pathogens transmitted by these vectors and rapidly expanding in their distributions, are increasingly prominent in public health agendas and pose significant health threats to humans (Staples et al., 2009; Gardner et al., 2012; Bhatt et al., 2013; Weaver and Lecuit, 2015). In common with previous work to map the global distributions of the dominant vectors of malaria (Sinka et al., 2010a, 2010b, 2011), the maps will improve efforts to understand the spatial epidemiology of associated arboviruses, and to predict how these could change in the future. Specifically, these maps may be used to prioritize surveillance for these vector species and the diseases caused by the viruses they transmit in areas where disease and entomological reporting remains poor. For example, in parts of Asia and Africa where there is a mismatch between predicted environmental suitability and reported occurrences, these maps could be used to determine whether the vector has yet to fill its niche or if it is present but has not been reported due to limited entomological surveillance. They may also be used to identify areas where the species could persist but has yet to be reported, in order to proactively prevent vector establishment.

The relative contributions of each of the environmental covariates to the global models concur with our theoretical and experimental understanding of each species' biology. Both species' distributions are highly dependent on the limiting factor temperature places on survival of the adult mosquitoes and on the gonotrophic cycle (Brady et al., 2013) (Table 2). The inclusion of a bespoke temperature suitability index (Brady et al., 2014), both in defining the pseudo-absences and as a covariate, allowed us to capture both geographic and temporal variations in the species-specific effects of temperature in a single variable, leading to improved predictive skill of the models. As both Ae. aegypti and Ae. albopictus lay their eggs in small water-filled containers (Morrison et al., 2004), it is encouraging that precipitation also has a strong influence on the model's predictions. The stronger influence of minimum precipitation for Ae. albopictus than for Ae. aegypti (16.1% vs 9.1%, Table 2) may reflect the former species' preference for non-domestic juvenile habitats, which are solely reliant on filling via precipitation. By contrast, Ae. aegypti primarily inhabits domestic water-holding containers (Scott et al., 2000) that are maintained in low-precipitation environments by water storage activities. The greater importance of enhanced vegetation index (EVI) for Ae. albopictus than for Ae. aegypti (15.3% vs 12.1%, Table 2) also supports the hypothesis that Ae. albopictus tends to prefer non-domestic juvenile sites (Morrison et al., 2004). This does not, however, rule out the possibility that the two species can overlap. Additional finer scale studies need to be conducted to investigate if competitive exclusion for hosts and/or habitat occurs between Ae. aegypti and Ae. albopictus. The effect of urbanicity was surprisingly low for both species (2% and 1.1% for Ae. albopictus and Ae. aegypti, respectively). As both species have been shown to inhabit a wide variety of urban and peri-urban settings with various degrees of intensity (Powell and Tabachnick, 2013; Li et al., 2014), it is likely that the simple urban/rural distinction of our urbanicity covariate did not sufficiently capture this variation and instead continuous covariates such as EVI allow to better distinguish the respective habitat types and were thus chosen more frequently by the model. Incorporating a larger set of covariates allowed us to investigate not only the effect of temperature on survival but for additional variance as shown in the relative influence plots (Figure 1—figure supplement 1). Future Aedes species distribution models could be improved by including a comprehensive global covariate that distinguishes human settlements using complex satellite imagery processing tools (Schneider, 2012).

Our maps are based on covariates where each 5 km × 5 km pixel represents yearly mean average values. We therefore produce maps that represent the long-term average distribution of both species. However, this does not allow us to directly infer seasonal patterns of distributions which might be of importance on the periphery of the species distributions. With a more temporally resolved dataset it may be possible to capture the effects of intra-annual seasonality on the species' distributions. Adding mechanistic determinants, such as survival, have previously been used to combine seasonal patterns with global distribution maps (Johansson et al., 2014). To make best use of the comprehensive set of data collected, we construct models and maps at a global scale, allowing the model to share information across the whole spectrum of environmental regions. However, given the scale at which this study was performed, there is always the possibility that variation in microclimate or local adaptive strategies of both species may have a significant impact in some locations.

Previous studies have discussed the risk of pathogen importation and autochthonous transmission of DENV and CHIKV in Europe and the Americas without comprehensively accounting for the distribution of the vectors (Bogoch et al., 2014; Schaffner and Mathis, 2014). These freely available vector distributions maps (http://goo.gl/Zl2P7J) can now be used as covariates to refine these studies and to generate high-resolution maps of the risk of possible local DENV and CHIKV transmission in currently non-endemic settings. Such maps would be useful for prioritizing surveillance in areas where there is a risk of disease importation. This will be especially important in areas where sporadic cases of related viruses have been reported, such as Europe, the United States, Argentina, and China (Rezza et al., 2007; Otero and Solari, 2010; Wu et al., 2010; Johansson, 2015).

Both Ae. aegypti and Ae. albopictus have a history of global expansion associated with trade and travel (Tatem et al., 2006; Brown et al., 2014; Gloria-Soria et al., 2014). Introductions of the species over long distances and between continents has been associated with international trade routes via shipping and overland spread driven by human movement and transport routes, both facilitated by the endophilic behavior of the two species (Nawrocki and Hawley, 1987; Tatem et al., 2006; Hofhuis et al., 2009). The global spread of the associated pathogens has undoubtedly been a consequence of increasing global connectedness. As these processes continue and the world becomes increasingly connected and urbanized, risk of importation and subsequent autochthonous transmission of DENV and CHIKV will continue to increase (Allwinn et al., 2008; Tomasello and Schlagenhauf, 2013; Khan et al., 2014; Messina et al., 2015). The true distribution of both species is influenced by a variety of factors, not just the ones presented here. Nevertheless, this study represents an important baseline for further refinements. For instance, our maps can be used to indicate areas where the species are likely to become established if introduced. Accurately predicting the future distributions of these species will also require model-based estimates of the rate at which these species colonize new areas. Such predictions can be informed by human and trade mobility patterns between endemic and non-endemic regions as well as data on the past spread of the vectors. Improving our ability to predict rates of vector importation will therefore be crucial to inferring future risk (Seebens et al., 2013).

Previous studies have provided crucial information on genetic variation both within and between populations of these two vector species (Brown et al., 2011). As the volume of georeferenced information on the population genetics of Ae. aegypti and Ae. albopictus increases, the potential to incorporate this information into mapping analyses to understand the current and future distribution of disease risk also increases. Phylogeographic analyses offer a unique way to infer the recent patterns of vector spread and to identify the major routes of importation (Allicock et al., 2012). This information is crucial to inform models that predict the risk of vector introductions.

Phylogenetic information could also be used to inform future iterations of the species distribution models used here by enabling the model to characterize and map environmental suitability for different vector subspecies. This could be particularly useful in the case of Ae. albopictus where genetic variation is known to underlie the ability to undergo diapause and therefore to overwinter in colder locations (Takumi et al., 2009). Mapping the distributions of distinct genetic subgroups could also improve our understanding of the complex interactions between mosquito vector populations and virus strains and how this relates to spatial variation in transmission intensity (Tsetsarkin et al., 2007; Vazeille et al., 2007; Tsetsarkin and Weaver, 2011; Zouache et al., 2014).

The maps presented comprise a contemporary estimate of the current and potential future distribution of Ae. aegypti and Ae. albopictus. As more occurrence data become available, these maps can be refined to incorporate recent importation and establishment events and corresponding improvements in predictions. By disseminating both the occurrence data and the predictive maps on an open-access basis we hope to facilitate both the future development of these maps and their uptake by the global public health community.

A BRT modelling approach was applied to derive probabilistic global environmental risk maps for Ae. aegypti and Ae. albopictus. BRT models are machine-learning model ensembles commonly used in species distribution modelling (SDM) and show strong predictive performance due to their ability to handle complex non-linear relationships between probability of species occurrence and multiple environmental correlates (Elith et al., 2006, 2008). Our model required the following sets of input data in order to make accurate predictions of the distribution of these two species: (i) a temperature suitability mask defining the fundamental limits of both species; (ii) a globally comprehensive dataset of geo-positioned occurrence points for both species; (iii) appropriate land-cover and environmental covariate datasets that help explain the current distribution of the species; and (iv) a set of species absence records that further refine the species range and reduce sampling bias. Details regarding the specific attributes of the model and data generation are outlined below and maps of each of the covariates are shown in Figure 1—figure supplement 2.

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Author details

1. Moritz UG Kraemer

   Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

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

   For correspondence

   [email protected]

   Competing interests

   No competing interests declared.

2. Marianne E Sinka

   Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   MES, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Kirsten A Duda

   Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   KAD, Acquisition of data

   Competing interests

   No competing interests declared.

4. Adrian QN Mylne

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

   Contribution

   AQNM, Acquisition of data

   Competing interests

   No competing interests declared.

5. Freya M Shearer

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

   Contribution

   FMS, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

6. Christopher M Barker

   Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, United States

   Contribution

   CMB, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0002-7941-346X

7. Chester G Moore

   Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, United States

   Contribution

   CGM, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   No competing interests declared.

8. Roberta G Carvalho

   National Dengue Control Program, Ministry of Health, Brasilia, Brazil

   Contribution

   RGC, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   No competing interests declared.

9. Giovanini E Coelho

   National Dengue Control Program, Ministry of Health, Brasilia, Brazil

   Contribution

   GEC, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   No competing interests declared.

10. Wim Van Bortel

    European Centre for Disease Prevention and Control, Stockholm, Sweden

    Contribution

    WVB, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    No competing interests declared.

11. Guy Hendrickx

    Avia-GIS, Zoersel, Belgium

    Contribution

    GH, Contributed unpublished essential data or reagents

    Competing interests

    No competing interests declared.

12. Francis Schaffner

    Avia-GIS, Zoersel, Belgium

    Contribution

    FS, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    No competing interests declared.

    "This ORCID iD identifies the author of this article:" 0000-0001-9166-7617

13. Iqbal RF Elyazar

    Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia

    Contribution

    IRFE, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    No competing interests declared.

14. Hwa-Jen Teng

    Center for Research, Diagnostics and Vaccine Development, Centers for Disease Control, Taipei, Taiwan

    Contribution

    H-JT, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    No competing interests declared.

15. 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.

16. Jane P Messina

    Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom

    Contribution

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

    Competing interests

    No competing interests declared.

17. David M Pigott

    1. Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
    2. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

    Contribution

    DMP, 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:" 0000-0002-6731-4034

18. Thomas W Scott

    1. Fogarty International Center, National Institutes of Health, Bethesda, United States
    2. Department of Entomology and Nematology, University of California, Davis, Davis, United States

    Contribution

    TWS, Conception and design, Drafting or revising the article

    Competing interests

    No competing interests declared.

19. David L Smith

    1. Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
    2. Fogarty International Center, National Institutes of Health, Bethesda, United States
    3. Sanaria Institute for Global Health and Tropical Medicine, Rockville, United States

    Contribution

    DLS, 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:" 0000-0003-4367-3849

20. GR William Wint

    Environmental Research Group Oxford, Department of Zoology, University of Oxford, Oxford, United Kingdom

    Contribution

    GRWW, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    No competing interests declared.

21. Nick Golding

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

    Contribution

    NG, 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:" 0000-0001-8916-5570

22. Simon I Hay

    1. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
    2. Fogarty International Center, National Institutes of Health, Bethesda, United States
    3. Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States

    Contribution

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

    For correspondence

    [email protected]

    Competing interests

    SIH: Reviewing editor, eLife.

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

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