With no apparent end in sight to the continued growth in global air travel, we must expect the continued appearance of disease-spreading vector invasions and vector-borne disease movement. Approaches that can inform decision makers on the risk factors behind these importations and onward spread risks can be used to focus surveillance and control efforts more efficiently. This paper and the VBD-AIR tool it describes show that multiple datasets on many aspects relating to the risk of movement of insect vectors and vector-borne diseases through the global air network can be compiled to provide such information through a user-friendly web tool. The principal function of the VBD-AIR tool is to provide an evidence base for assessing the role of air travel in the spread of vector-borne diseases and their vectors through available spatial data. The VBD-AIR tool is designed with a wide range of potential users in mind. These include planners and decisions makers based in state and local government, in particular, those at international and domestic airports tasked with planning for and dealing with health risks and allocating limited resources. It is clear from exploration of outputs from VBD-AIR that each region, airport and flight route has a differing risk profile in terms of disease and vector importation, determined largely by the structure of the air network and its congruence with infectious disease distributions and outbreaks and vector distributions and seasonality, yet this is rarely quantified and used when control methods and surveillance are considered.
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
]. 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:
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
]. 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.
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
The development of stochastic analogues of existing deterministic approaches to modelling of disease movement through air networks that are capable of handling input parameter distributions rather than simple mean values, and provide measures of uncertainty with output forecasts. The process of disease importation is a stochastic process, and, depending upon the disease, each relevant variable (e.g. seasonal variations in transmission, passenger numbers, and infection risk) can exhibit substantial variations from the mean and include uncertainty in the way they are measured. By simulating risks of importation from literature-derived probability distributions for each variable, improved and more informative model outputs could be produced that would enable the user to better understand and manage the uncertainties inherent in forecasts (e.g. [49