Substantial evidence exists that documents examples of both vector-borne diseases and the vectors that carry them being transported between distant locations via air travel [10,11]. The global air network enables many of the world’s most isolated and diverse ecosystems to become connected and aids the movement of organisms, including disease vectors, to new habitats where they can become damaging invasive species, economically and health-wise [4,12,13]. Mosquitoes can survive moderately high atmospheric pressures aboard aircraft [14] and can be transported alive between international destinations, even in wheel bays [15]. However, air travel likely plays a much more significant role in moving the vector-borne disease (via infected passengers) than in moving the vector. It provides rapid and wide-reaching connections between outbreaks or high levels of endemicity and susceptible vector populations elsewhere in the world. Up to 8% of travellers to the developing world become ill enough to seek healthcare upon returning home, with a significant proportion of these suffering from vector-borne infections [11]. Around 10,000 cases of imported malaria to high income countries are reported each year, but the true figure may be over 25,000 [16]. With imported vector-borne infections placing a financial and operational burden on health systems in non-endemic countries (e.g. [17]), as well as the risk of onward transmission and even establishment, the development of tools for quantifying the spatiotemporal risks of importation of both vector-borne diseases and the vectors that carry them can be valuable for assessing and guiding the allocation of limited control and surveillance resources.

Recent efforts in the field of global mapping of vector-borne diseases (e.g. [18,19]) and the vectors that carry them (e.g. [20-22]), generally through using sample data of known disease or vector presence in combination with environmental covariates, now provide strong evidence bases for determining vector-borne disease risks across the world. The linkage of these disease and vector distribution maps with air travel network data offers great potential for infection importation risk assessment and the modelling of vector-borne disease spread. Such distribution maps, however, represent static pictures of relatively long term (>1year) disease prevalence and vector presence. If importation and onward spread risks are to be accurately quantified, the substantial climate-driven seasonal fluctuations in disease risk and vector densities need to be accounted for [23]. If a vector or an infected individual arrives in a new location via air travel, the risk of the vector establishing or the infection being passed on to local vector populations is often dependent upon the month of arrival. The arrival of an individual infected in a mosquito-borne disease outbreak occurring in January in the southern hemisphere (e.g. Sao Paulo) into a city in the northern hemisphere (e.g. New York) will present little risk of onward transmission, due to the cold January climate being not conducive for mosquito activity. However, a similar arrival from e.g. India (where climatic conditions may be suitable for year-round transmission) in July presents a much greater risk [23]. By utilizing gridded climate data to measure climatic similarity between origin and destination locations with known presence of a suitable vector, and adjusting for flight passenger numbers as an additional measure of risk, these factors can be accounted for [4,24].

Here we introduce the Vector-Borne Disease Airline Importation Risk tool (VBD-AIR; http://www.vbd-air.com), which brings together global vector-borne disease and vector distribution maps, climate data and air network traffic information to inform on the spatiotemporal risks of disease and vector importation. The VBD-AIR tool takes the form of an interactive online interface and is targeted at users with interests in specific airports or regions, and the risks to those locations of vector-borne disease importation and onward spread, or exotic vector importation and establishment.

The visualization of the air travel network within VBD-AIR follows Schneiderman’s principle for data visualization: Overview first, zoom and filter, then details-on-demand [42]. The web interface provides a map view of the flight connections from endemic disease or vector presence areas to a user-selected airport, with a predicted disease risk or vector presence map overlaid (Figure​(Figure5).5). The user can view and navigate through the flight routes from each of the airports within the endemic or vector presence regions. As the user selects their choice of risk assessment type (imported disease risks, onward transmission risks or imported vector risks – see Figure​Figure4),4), the view supports an automatic colouring scheme to render the origin airports and the routes based on the user input and the objectives for risk assessment. Moreover, when the user clicks on an airport, detailed location information for that airport is provided. The interface is also well equipped with a comprehensive user guide, a short tutorial and integrated pop-up help windows so that the user can get a better understanding of the tool functionality, datasets and outputs.

For both the “direct flight” and “indirect flight” options (Figure​(Figure4),4), users are required to input the name or city of their airport of interest, disease of interest, and their related choice of disease distribution or vector presence map. The direct flight options provide “imported disease risks” and “onward transmission risks” functions if the user selects a disease map, and “imported vector risks” if the user selects a vector map. The imported disease risk option provides the top 10 routes by route capacity from disease endemic areas and the top 10 routes by risk-scaled traffic from disease endemic areas. The onward transmission risks and imported vector risks provide top 10 routes by climatic similarity scaled traffic and top 10 routes by climate similarity and disease/vector risk scaled traffic. The option to produce a more detailed PDF report output is provided to enable users to examine output statistics in greater detail and view longitudinal data. An option is also available to view indirect flights to the airport of interest, which highlights the possible routes from disease endemic areas via a single flight transfer, and a list of intermediate airports.

The VBD-AIR tool shows that a multi-disciplinary approach, which draws on a variety of spatial data on factors known to influence the spread of vectors and the diseases they carry, offers potential for assessing the risk of disease importation. A range of uncertainties and limitations do still exist in the datasets and outputs presented however, and users are made aware of these within the full user guide and throughout the information boxes within the tool. Firstly, VBD-AIR considers only direct flights and their capacities within metric calculations, rather than actual passenger numbers or stopovers, and users are made aware of the uncertainties that this entails [43]. Within the disease and vector distribution modeling processes, uncertainties are inherent throughout [44], particularly in those regions with little field data to inform predictions. Moreover, we have treated the vector distributions as single homogenous types of mosquito, yet competition, competence, adaptation and preferences can vary widely across their global distributions [20-22,33]. Accurate data on outbreak locations and sizes, as with many diseases, are also difficult to obtain to be sure of comprehensive assessments of risk, however, improvements in global surveillance and the rapid availability of data are improving (e.g.[45]). Also, the distance between two airports and the population size of these airports have not been explicitly incorporated in the risk assessments here. Proximity to endemic area plays an important role in vector-borne disease importation [46], while city population size can be utilized to estimate the rate of disease movement between pairs of airports [47]. Further the use of climatic similarity measures may not be appropriate to certain contexts, such as those where arid climatic conditions prompt increased water storage, leading to rises in vector-borne disease risks, rather than the decreases that may be indicated by CEDs. Finally, how to interpret and act upon the kind of relative risks identified in VBD-AIR is a challenge yet to be overcome, but various approaches to mitigating risks are presented within the tool text boxes and user guide [3].

Future updates are planned to VBD-AIR that will expand its capabilities, as the MVC framework is designed to be flexible for robust expansion. These will include: (i) Regular updates of the disease and vector distribution maps, as new survey data and outbreak reports become available; (ii) updates to the flight data as new information on flight capacities and routes becomes available; (iii) additional scenario-related functionality will be built into the tool based on a set of control and mitigation options. This will provide users with guidance on approaches to limiting imported cases and vectors, and mitigating the effects of vector establishment or onward disease transmission; (iv) the interactive incorporation of the accessibility datasets. At present this represents a simple visualization of access to provide context, and upcoming extensions will focus on building in measures of access to better capture airport catchment areas and estimate likely regions impacted by imported cases or vectors; (v) the incorporation of extra vector-borne diseases and vectors. The choice of additional diseases and vectors will depend upon availability of sufficient spatial data for mapping or validated global maps. Candidate diseases include leishmaniasis, Rift Valley fever and chagas disease.

It is envisioned that future research beyond the simple updates and tool expansions described above will build upon VBD-AIR to continue to improve quantification of these aspects, drawing on newly-developed spatial datasets and mathematical models of transmission, to provide an evidence base to enable airports, airlines, and public health officials to assess the appropriateness and efficacy of current control, surveillance and treatment practices, and tailor strategies to these differing risk profiles for each disease, route and airport. Three specific areas of research should be examined:

(i) Constructing geospatial information databases on global endemic disease distributions, and building a framework for the rapid inclusion of outbreak reporting data from surveillance databases such as Health Map (http://www.healthmap.org) [41]. Increasingly, spatial information on the prevalence of directly-transmitted and insect-borne diseases are being made available, and approaches for using these data to build distribution maps and dynamic transmission models are following. The potential of combining such data with air traffic data for forecasting disease movements has been shown for a handful of diseases in specific locations, but this potential has yet to be realized at global scales.

(ii) Increasing the sophistication of flight passenger movement data and models. Existing models of disease movement over air networks are generally driven by flight capacity and direct flight data [48], missing valuable information on stopovers, actual passenger numbers and lengths of stay. Sample datasets on ticket sales and flight occupancy (e.g. US Transtats T100 and DB1B data http://www.transtats.bts.gov ) should be utilized to derive models that can better replicate realistic passenger flows, for integration with the disease risk data [43]. Also, the incorporate of proximity measure (such as geographical distance or flight time between pairs of airports) and population information which airports serve is likely to facilitate the estimations of the actual travel flows between two airports [46,47].