On the treatment of airline travelers in mathematical models

International travel-related control measures to contain the COVID-19 pandemic: a rapid review

BACKGROUND

In late 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers.

OBJECTIVES

To assess the effectiveness of international travel-related control measures during the COVID-19 pandemic on infectious disease transmission and screening-related outcomes.

SEARCH METHODS

We searched MEDLINE, Embase and COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO Global Database on COVID-19 Research to 13 November 2020.

SELECTION CRITERIA

We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across international borders during the COVID-19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID-19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements.

MAIN RESULTS

Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission. Most studies compared travel-related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects. Below we outline the results for each intervention category by illustrating the findings from selected outcomes. Travel restrictions reducing or stopping cross-border travel (31 modelling studies) The studies assessed cases avoided and shift in epidemic development. We found very low-certainty evidence for a reduction in COVID-19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect. There was very low-certainty evidence that cross-border travel controls can slow the spread of COVID-19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure. Screening at borders (13 modelling studies; 13 observational studies) Screening measures covered symptom/exposure-based screening or test-based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely. For symptom/exposure-based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate-certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low-certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low-certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low-certainty evidence), although all but one study observed this proportion to be less than 54%. For test-based screening, one modelling study provided very low-certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low-certainty evidence). Quarantine (12 modelling studies) The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low- to low-certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low-certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance. Quarantine and screening at borders (7 modelling studies; 4 observational studies) The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low- to low-certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low-certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine.

AUTHORS' CONCLUSIONS

With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross-border travel and quarantine of travellers, there is a lack of 'real-world' evidence. The certainty of the evidence for most travel-related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure-based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure-based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel-related control measures from a societal perspective.

Travel-related control measures to contain the COVID-19 pandemic: a rapid review

BACKGROUND

In late 2019, first cases of coronavirus disease 2019, or COVID-19, caused by the novel coronavirus SARS-CoV-2, were reported in Wuhan, China. Subsequently COVID-19 spread rapidly around the world. To contain the ensuing pandemic, numerous countries have implemented control measures related to international travel, including border closures, partial travel restrictions, entry or exit screening, and quarantine of travellers.

OBJECTIVES

To assess the effectiveness of travel-related control measures during the COVID-19 pandemic on infectious disease and screening-related outcomes.

SEARCH METHODS

We searched MEDLINE, Embase and COVID-19-specific databases, including the WHO Global Database on COVID-19 Research, the Cochrane COVID-19 Study Register, and the CDC COVID-19 Research Database on 26 June 2020. We also conducted backward-citation searches with existing reviews.

SELECTION CRITERIA

We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across national borders during the COVID-19 pandemic. We also included studies concerned with severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) as indirect evidence. Primary outcomes were cases avoided, cases detected and a shift in epidemic development due to the measures. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome.

DATA COLLECTION AND ANALYSIS

One review author screened titles and abstracts; all excluded abstracts were screened in duplicate. Two review authors independently screened full texts. One review author extracted data, assessed risk of bias and appraised study quality. At least one additional review author checked for correctness of all data reported in the 'Risk of bias' assessment, quality appraisal and data synthesis. For assessing the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, ROBINS-I for observational ecological studies and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed certainty of evidence with GRADE, and the review author team discussed ratings.

MAIN RESULTS

We included 40 records reporting on 36 unique studies. We found 17 modelling studies, 7 observational screening studies and one observational ecological study on COVID-19, four modelling and six observational studies on SARS, and one modelling study on SARS and MERS, covering a variety of settings and epidemic stages. Most studies compared travel-related control measures against a counterfactual scenario in which the intervention measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of travel restrictions, or a combination of measures. There were concerns with the quality of many modelling studies and the risk of bias of observational studies. Many modelling studies used potentially inappropriate assumptions about the structure and input parameters of models, and failed to adequately assess uncertainty. Concerns with observational screening studies commonly related to the reference test and the flow of the screening process. Studies on COVID-19 Travel restrictions reducing cross-border travel Eleven studies employed models to simulate a reduction in travel volume; one observational ecological study assessed travel restrictions in response to the COVID-19 pandemic. Very low-certainty evidence from modelling studies suggests that when implemented at the beginning of the outbreak, cross-border travel restrictions may lead to a reduction in the number of new cases of between 26% to 90% (4 studies), the number of deaths (1 study), the time to outbreak of between 2 and 26 days (2 studies), the risk of outbreak of between 1% to 37% (2 studies), and the effective reproduction number (1 modelling and 1 observational ecological study). Low-certainty evidence from modelling studies suggests a reduction in the number of imported or exported cases of between 70% to 81% (5 studies), and in the growth acceleration of epidemic progression (1 study). Screening at borders with or without quarantine Evidence from three modelling studies of entry and exit symptom screening without quarantine suggests delays in the time to outbreak of between 1 to 183 days (very low-certainty evidence) and a detection rate of infected travellers of between 10% to 53% (low-certainty evidence). Six observational studies of entry and exit screening were conducted in specific settings such as evacuation flights and cruise ship outbreaks. Screening approaches varied but followed a similar structure, involving symptom screening of all individuals at departure or upon arrival, followed by quarantine, and different procedures for observation and PCR testing over a period of at least 14 days. The proportion of cases detected ranged from 0% to 91% (depending on the screening approach), and the positive predictive value ranged from 0% to 100% (very low-certainty evidence). The outcomes, however, should be interpreted in relation to both the screening approach used and the prevalence of infection among the travellers screened; for example, symptom-based screening alone generally performed worse than a combination of symptom-based and PCR screening with subsequent observation during quarantine. Quarantine of travellers Evidence from one modelling study simulating a 14-day quarantine suggests a reduction in the number of cases seeded by imported cases; larger reductions were seen with increasing levels of quarantine compliance ranging from 277 to 19 cases with rates of compliance modelled between 70% to 100% (very low-certainty evidence).

AUTHORS' CONCLUSIONS

With much of the evidence deriving from modelling studies, notably for travel restrictions reducing cross-border travel and quarantine of travellers, there is a lack of 'real-life' evidence for many of these measures. The certainty of the evidence for most travel-related control measures is very low and the true effects may be substantially different from those reported here. Nevertheless, some travel-related control measures during the COVID-19 pandemic may have a positive impact on infectious disease outcomes. Broadly, travel restrictions may limit the spread of disease across national borders. Entry and exit symptom screening measures on their own are not likely to be effective in detecting a meaningful proportion of cases to prevent seeding new cases within the protected region; combined with subsequent quarantine, observation and PCR testing, the effectiveness is likely to improve. There was insufficient evidence to draw firm conclusions about the effectiveness of travel-related quarantine on its own. Some of the included studies suggest that effects are likely to depend on factors such as the stage of the epidemic, the interconnectedness of countries, local measures undertaken to contain community transmission, and the extent of implementation and adherence.

The use and reporting of airline passenger data for infectious disease modelling: a systematic review

BackgroundA variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources.AimWe conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology.MethodsArticles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies' reproducibility assessed.ResultsWe identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible.LimitationsBy limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus.ConclusionWe recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility.

Germs on a Plane: The Transmission and Risks of Airplane-Borne Diseases

This research explored the role of air travel in the spread of infectious diseases, specifically severe acute respiratory syndrome (SARS), H1N1, Ebola, and pneumonic plague. Air travel provides the means for such diseases to spread internationally at extraordinary rates because infected passengers jump from coast to coast and continent to continent within hours. Outbreaks of diseases that spread from person to person test the effectiveness of current public health responses. This research used a mixed methods approach, including use of the Spatiotemporal Epidemiological Modeler, to model the spread of diseases, evaluate the impact of air travel on disease spread, and analyze the effectiveness of different public health strategies and travel policies. Modeling showed that the spread of Ebola and pneumonic plague is minimal and should not be a major air travel concern if an individual becomes infected. H1N1 and SARS have higher infection rates and air travel will facilitate the spread of disease nationally and internationally. To contain the spread of infectious diseases, aviation and public health authorities should establish tailored preventive measures at airports, capture contact information for ticketed passengers, expand the definition of "close contact," and conduct widespread educational programs. The measures will put in place a foundation for containing the spread of infectious diseases via air travel and minimize the panic and economic consequences that may occur during an outbreak.

Estimating the risk of Dengue, Chikungunya and Zika outbreaks in a large European city

Outbreaks of arbovirus infections vectored by invasive Aedes albopictus have already occurred and are predicted to become increasingly frequent in Southern Europe. We present a probabilistic model to assess risk of arbovirus outbreaks based on incident cases worldwide, on the probability of arrival of infected travelers, and on the abundance of the vector species. Our results show a significant risk of Chikungunya outbreak in Rome from mid June to October in simulations with high human biting rates (i.e. when ≥50% of the population is bitten every day). The outbreak risk is predicted to be highest for Chikungunya and null for Zika. Simulated increase of incident cases in selected endemic countries has no major impact on the outbreak risk. The model correctly estimated the number of imported cases and can be easily adapted to other urban areas where Ae. albopictus is the only potential vector present.

Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015

Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries. This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations, Chinese travelers and local residents, over a decade of 2005-15. We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly. Following the growth of air traffic, the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015. This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission. Compared to the monthly occurrence of dengue reported in China, our model performed robustly for importation and onward transmission risk estimates. The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions.

Tracing Airline Travelers for a Public Health Investigation: Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Infection in the United States, 2014

OBJECTIVE

CDC routinely conducts contact investigations involving travelers on commercial conveyances, such as aircrafts, cargo vessels, and cruise ships.

METHODS

The agency used established systems of communication and partnerships with other federal agencies to quickly provide accurate traveler contact information to states and jurisdictions to alert contacts of potential exposure to two travelers with Middle East Respiratory Syndrome Coronavirus (MERS-CoV) who had entered the United States on commercial flights in April and May 2014.

RESULTS

Applying the same process used to trace and notify travelers during routine investigations, such as those for tuberculosis or measles, CDC was able to notify most travelers of their potential exposure to MERS-CoV during the first few days of each investigation.

CONCLUSION

To prevent the introduction and spread of newly emerging infectious diseases, travelers need to be located and contacted quickly.

Modeling Mosquito-Borne Disease Spread in U.S. Urbanized Areas: The Case of Dengue in Miami

Expansion of mosquito-borne pathogens into more temperate regions of the world necessitates tools such as mathematical models for understanding the factors that contribute to the introduction and emergence of a disease in populations naïve to the disease. Often, these models are not developed and analyzed until after a pathogen is detected in a population. In this study, we develop a spatially explicit stochastic model parameterized with publicly available U.S. Census data for studying the potential for disease spread in Urbanized Areas of the United States. To illustrate the utility of the model, we specifically study the potential for introductions of dengue to lead to autochthonous transmission and outbreaks in a population representative of the Miami Urbanized Area, where introductions of dengue have occurred frequently in recent years. We describe seasonal fluctuations in mosquito populations by fitting a population model to trap data provided by the Miami-Dade Mosquito Control Division. We show that the timing and location of introduced cases could play an important role in determining both the probability that local transmission occurs as well as the total number of cases throughout the entire region following introduction. We show that at low rates of clinical presentation, small outbreaks of dengue could go completely undetected during a season, which may confound mitigation efforts that rely upon detection. We discuss the sensitivity of the model to several critical parameter values that are currently poorly characterized and motivate the collection of additional data to strengthen the predictive power of this and similar models. Finally, we emphasize the utility of the general structure of this model in studying mosquito-borne diseases such as chikungunya and Zika virus in other regions.

Modeling monthly flows of global air travel passengers: An open-access data resource

The global flow of air travel passengers varies over time and space, but analyses of these dynamics and their integration into applications in the fields of economics, epidemiology and migration, for example, have been constrained by a lack of data, given that air passenger flow data are often difficult and expensive to obtain. Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). By refining an annual-scale model of Huang et al. (2013), we developed a set of Poisson regression models to predict monthly passenger volumes between directly connected airports during 2010. The models not only performed well in the United States with an overall accuracy of 93%, but also showed a reasonable confidence in estimating air passenger volumes in other regions of the world. Using the model outcomes, this research studied the spatio-temporal dynamics in the world airline network (WAN) that previous analyses were unable to capture. Findings on the monthly variation of WAN offer new knowledge for dynamic planning and strategy design to address global issues, such as disease pandemics and climate change.

An open-access modeled passenger flow matrix for the global air network in 2010

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.

Skip the trip: air travelers' behavioral responses to pandemic influenza

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over "swine flu," as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.

Web-based GIS: the vector-borne disease airline importation risk (VBD-AIR) tool

Background

Over the past century, the size and complexity of the air travel network has increased dramatically. Nowadays, there are 29.6 million scheduled flights per year and around 2.7 billion passengers are transported annually. The rapid expansion of the network increasingly connects regions of endemic vector-borne disease with the rest of the world, resulting in challenges to health systems worldwide in terms of vector-borne pathogen importation and disease vector invasion events. Here we describe the development of a user-friendly Web-based GIS tool: the Vector-Borne Disease Airline Importation Risk Tool (VBD-AIR), to help better define the roles of airports and airlines in the transmission and spread of vector-borne diseases.

Methods

Spatial datasets on modeled global disease and vector distributions, as well as climatic and air network traffic data were assembled. These were combined to derive relative risk metrics via air travel for imported infections, imported vectors and onward transmission, and incorporated into a three-tier server architecture in a Model-View-Controller framework with distributed GIS components. A user-friendly web-portal was built that enables dynamic querying of the spatial databases to provide relevant information.

Results

The VBD-AIR tool constructed enables the user to explore the interrelationships among modeled global distributions of vector-borne infectious diseases (malaria. dengue, yellow fever and chikungunya) and international air service routes to quantify seasonally changing risks of vector and vector-borne disease importation and spread by air travel, forming an evidence base to help plan mitigation strategies. The VBD-AIR tool is available at http://www.vbd-air.com.

Conclusions

VBD-AIR supports a data flow that generates analytical results from disparate but complementary datasets into an organized cartographical presentation on a web map for the assessment of vector-borne disease movements on the air travel network. The framework built provides a flexible and robust informatics infrastructure by separating the modules of functionality through an ontological model for vector-borne disease. The VBD‒AIR tool is designed as an evidence base for visualizing the risks of vector-borne disease by air travel for a wide range of users, including planners and decisions makers based in state and local government, and in particular, those at international and domestic airports tasked with planning for health risks and allocating limited resources.

Assessing the risk of international spread of yellow fever virus: a mathematical analysis of an urban outbreak in Asuncion, 2008

Yellow fever virus (YFV), a mosquito-borne virus endemic to tropical Africa and South America, is capable of causing large urban outbreaks of human disease. With the ease of international travel, urban outbreaks could lead to the rapid spread and subsequent transmission of YFV in distant locations. We designed a stochastic metapopulation model with spatiotemporally explicit transmissibility scenarios to simulate the global spread of YFV from a single urban outbreak by infected airline travelers. In simulations of a 2008 outbreak in Asunción, Paraguay, local outbreaks occurred in 12.8% of simulations and international spread in 2.0%. Using simple probabilistic models, we found that local incidence, travel rates, and basic transmission parameters are sufficient to assess the probability of introduction and autochthonous transmission events. These models could be used to assess the risk of YFV spread during an urban outbreak and identify locations at risk for YFV introduction and subsequent autochthonous transmission.

Air travel and vector-borne disease movement

Recent decades have seen substantial expansions in the global air travel network and rapid increases in traffic volumes. The effects of this are well studied in terms of the spread of directly transmitted infections, but the role of air travel in the movement of vector-borne diseases is less well understood. Increasingly however, wider reaching surveillance for vector-borne diseases and our improving abilities to map the distributions of vectors and the diseases they carry, are providing opportunities to better our understanding of the impact of increasing air travel. Here we examine global trends in the continued expansion of air transport and its impact upon epidemiology. Novel malaria and chikungunya examples are presented, detailing how geospatial data in combination with information on air traffic can be used to predict the risks of vector-borne disease importation and establishment. Finally, we describe the development of an online tool, the Vector-Borne Disease Airline Importation Risk (VBD-Air) tool, which brings together spatial data on air traffic and vector-borne disease distributions to quantify the seasonally changing risks for importation to non-endemic regions. Such a framework provides the first steps towards an ultimate goal of adaptive management based on near real time flight data and vector-borne disease surveillance.