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

Understanding the impact of network structure on air travel pattern at different scales

This study examines the global air travel demand pattern using complex network analysis. Using the data for the top 50 airports based on passenger volume rankings, we investigate the relationship between network measures of nodes (airports) in the global flight network and their passenger volume. The analysis explores the network measures at various spatial scales, from individual airports to metropolitan areas and countries. Different attributes, such as flight route length and the number of airlines, are considered in the analysis. Certain attributes are found to be more relevant than others, and specific network measure models are found to better capture the dynamics of global air travel demand than others. Among the models, PageRank is found to be the most correlated with total passenger volume. Moreover, distance-based measures perform worse than the ones emphasising the number of airlines, particularly those counting the number of airlines operating a route, including codeshare. Using the PageRank score weighted by the number of airlines, we find that airports in Asian cities tend to have more traffic than expected, while European and North American airports have the potential to attract more passenger volume given their connectivity pattern. Additionally, we combine the network measures with socio-economic variables such as population and GDP to show that the network measures could greatly augment the traditional approaches to modelling and predicting air travel demand. We'll also briefly discuss the implications of the findings in this study for airport planning and airline industry strategy.

Sovereign debt responses to the COVID-19 pandemic

We utilize the global natural experiment created by the COVID-19 outbreak to identify sovereign borrowing capacity in time of need and its determinants. First, we demonstrate that the pandemic creates exogeneous shocks to sovereign borrowing needs-governments borrowed more when hit by more severe pandemic shocks. Second, we show that credible fiscal rules enhance sovereign borrowing capacity, while unsustainable debts in terms of high debt-to-GDP ratio, rollover risk, and sovereign default risk weaken it. Third, we find that, in response to the same pandemic shock, sovereign spreads increase more in emerging economies than advanced economies though the former borrow less during the pandemic. Finally, further analysis reveals that pegged exchange rate regimes, open capital accounts, and monetary dependence improve emerging economies' borrowing capacity.

A Regional View of Passenger Air Link Evolution in Brazil

Passenger flows among cities, at both the domestic and international levels and in different countries and regions, have been studied for forecasting purposes. This paper seeks not a forecasting mechanism, but to understand, by analyzing passenger origins and destinations by Brazilian sub-region, how Brazil’s domestic air passenger network links have evolved. Using income, population, and fare price as inputs, and seats sold as output, air link performance is examined by data envelopment analysis to discuss the regional link of domestic passenger traffic in Brazil and its dynamics, considering two specific years. The findings indicate that, although the highest passenger flow density is concentrated in Brazil’s Southeast region, performance by emerging origins and destinations (O-Ds), such as those connecting the Northeast, display more substantial strength indices and advances (Malmquist analysis). The analysis of specific links was also important, which showed that important Brazilian airports are not necessarily more competent in generating trips. The Catch-Up indicator for innovation reveals the weak point in Brazil’s air transport network. Although some airports enjoy strong networkability, they do not correspond in passenger origin or destination strength.

Estimating the potential for global dissemination of pandemic pathogens using the global airline network and healthcare development indices

Pandemics have the potential to incur significant health and economic impacts, and can reach a large number of countries from their origin within weeks. Early identification and containment of a newly emerged pandemic within the source country is key for minimising global impact. To identify a country's potential to control and contain a pathogen with pandemic potential, we compared the quality of a country's healthcare system against its global airline connectivity. Healthcare development was determined using three multi-factorial indices, while detailed airline passenger data was used to identify the global connectivity of all countries. Proximities of countries to a putative 'Worst Case Scenario' (extreme high-connectivity and low-healthcare development) were calculated. We found a positive relationship between a country's connectivity and healthcare metrics. We also identified countries that potentially pose the greatest risk for pandemic dissemination, notably Dominican Republic, India and Pakistan. China and Mexico, both sources of recent influenza and coronavirus pandemics were also identified as among the highest risk countries. Collectively, lower-middle and upper-middle income countries represented the greatest risk, while high income countries represented the lowest risk. Our analysis represents an alternative approach to identify countries where increased within-country disease surveillance and pandemic preparedness may benefit global health.

Global holiday datasets for understanding seasonal human mobility and population dynamics

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010-2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

Classi-Fly: Inferring Aircraft Categories from Open Data

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically, these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category.

armasuisse Science + Technology , the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than 2 million aircraft. We show that we can obtain the correct aircraft category with an accuracy of greater than 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect particular sensitive aircraft such as police and surveillance aircraft using this method.

Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease

Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.

Air Passenger Travel and International Surveillance Data Predict Spatiotemporal Variation in Measles Importations to the United States

Measles incidence in the United States has grown dramatically, as vaccination rates are declining and transmission internationally is on the rise. Because imported cases are necessary drivers of outbreaks in non-endemic settings, predicting measles outbreaks in the US depends on predicting imported cases. To assess the predictability of imported measles cases, we performed a regression of imported measles cases in the US against an inflow variable that combines air travel data with international measles surveillance data. To understand the contribution of each data type to these predictions, we repeated the regression analysis with alternative versions of the inflow variable that replaced each data type with averaged values and with versions of the inflow variable that used modeled inputs. We assessed the performance of these regression models using correlation, coverage probability, and area under the curve statistics, including with resampling and cross-validation. Our regression model had good predictive ability with respect to the presence or absence of imported cases in a given state in a given year (area under the curve of the receiver operating characteristic curve (AUC) = 0.78) and the magnitude of imported cases (Pearson correlation = 0.84). By comparing alternative versions of the inflow variable averaging over different inputs, we found that both air travel data and international surveillance data contribute to the model's ability to predict numbers of imported cases and individually contribute to its ability to predict the presence or absence of imported cases. Predicted sources of imported measles cases varied considerably across years and US states, depending on which countries had high measles activity in a given year. Our results emphasize the importance of the relationship between global connectedness and the spread of measles. This study provides a framework for predicting and understanding imported case dynamics that could inform future studies and outbreak prevention efforts.

Modeling and interpreting the COVID-19 intervention strategy of China: A human mobility view

The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China’s mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies.

Modeling Commodity Flow in the Context of Invasive Species Spread: Study of Tuta absoluta in Nepal

Trade and transport of goods is widely accepted as a primary pathway for the introduction and dispersal of invasive species. However, understanding commodity flows remains a challenge owing to its complex nature, unavailability of quality data, and lack of systematic modeling methods. A robust network-based approach is proposed to model seasonal flow of agricultural produce and examine its role in pest spread. It is applied to study the spread of Tuta absoluta, a devastating pest of tomato in Nepal. Further, the long-term establishment potential of the pest and its economic impact on the country are assessed. Our analysis indicates that regional trade plays an important role in the spread of T. absoluta. The economic impact of this invasion could range from USD 17-25 million. The proposed approach is generic and particularly suited for data-poor scenarios.

Estimating COVID-19 outbreak risk through air travel

BACKGROUND

Substantial limitations have been imposed on passenger air travel to reduce transmission of severe acute respiratory syndrome coronavirus 2 between regions and countries. However, as case numbers decrease, air travel will gradually resume. We considered a future scenario in which case numbers are low and air travel returns to normal. Under that scenario, there will be a risk of outbreaks in locations worldwide due to imported cases. We estimated the risk of different locations acting as sources of future coronavirus disease 2019 outbreaks elsewhere.

METHODS

We use modelled global air travel data and population density estimates from locations worldwide to analyse the risk that 1364 airports are sources of future coronavirus disease 2019 outbreaks. We use a probabilistic, branching-process-based approach that considers the volume of air travelers between airports and the reproduction number at each location, accounting for local population density.

RESULTS

Under the scenario we model, we identify airports in East Asia as having the highest risk of acting as sources of future outbreaks. Moreover, we investigate the locations most likely to cause outbreaks due to air travel in regions that are large and potentially vulnerable to outbreaks: India, Brazil and Africa. We find that outbreaks in India and Brazil are most likely to be seeded by individuals travelling from within those regions. We find that this is also true for less vulnerable regions, such as the United States, Europe and China. However, outbreaks in Africa due to imported cases are instead most likely to be initiated by passengers travelling from outside the continent.

CONCLUSIONS

Variation in flight volumes and destination population densities creates a non-uniform distribution of the risk that different airports pose of acting as the source of an outbreak. Accurate quantification of the spatial distribution of outbreak risk can therefore facilitate optimal allocation of resources for effective targeting of public health interventions.

Modeling human migration across spatial scales in Colombia

Human mobility, both short and long term, are important considerations in the study of numerous systems. Economic and technological advances have led to a more interconnected global community, further increasing the need for considerations of human mobility. While data on human mobility are better recorded in many developed countries, availability of such data remains limited in many low- and middle-income countries around the world, particularly at the fine temporal and spatial scales required by many applications. In this study, we used 5-year census-based internal migration microdata for 32 departments in Colombia (i.e., Admin-1 level) to develop a novel spatial interaction modeling approach for estimating migration, at a finer spatial scale, among the 1,122 municipalities in the country (i.e., Admin-2 level). Our modeling approach addresses a significant lack of migration data at administrative unit levels finer than those at which migration data are typically recorded. Due to the widespread availability of census-based migration microdata at the Admin-1 level, our modeling approach opens up for the possibilities of modeling migration patterns at Admin-2 and Admin-3 levels across many other countries where such data are currently lacking.

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.

Deposition of respiratory virus pathogens on frequently touched surfaces at airports

BACKGROUND

International and national travelling has made the rapid spread of infectious diseases possible. Little information is available on the role of major traffic hubs, such as airports, in the transmission of respiratory infections, including seasonal influenza and a pandemic threat. We investigated the presence of respiratory viruses in the passenger environment of a major airport in order to identify risk points and guide measures to minimize transmission.

METHODS

Surface and air samples were collected weekly at three different time points during the peak period of seasonal influenza in 2015-16 in Finland. Swabs from surface samples, and air samples were tested by real-time PCR for influenza A and B viruses, respiratory syncytial virus, adenovirus, rhinovirus and coronaviruses (229E, HKU1, NL63 and OC43).

RESULTS

Nucleic acid of at least one respiratory virus was detected in 9 out of 90 (10%) surface samples, including: a plastic toy dog in the children's playground (2/3 swabs, 67%); hand-carried luggage trays at the security check area (4/8, 50%); the buttons of the payment terminal at the pharmacy (1/2, 50%); the handrails of stairs (1/7, 14%); and the passenger side desk and divider glass at a passport control point (1/3, 33%). Among the 10 respiratory virus findings at various sites, the viruses identified were: rhinovirus (4/10, 40%, from surfaces); coronavirus (3/10, 30%, from surfaces); adenovirus (2/10, 20%, 1 air sample, 1 surface sample); influenza A (1/10, 10%, surface sample).

CONCLUSIONS

Detection of pathogen viral nucleic acids indicates respiratory viral surface contamination at multiple sites associated with high touch rates, and suggests a potential risk in the identified airport sites. Of the surfaces tested, plastic security screening trays appeared to pose the highest potential risk, and handling these is almost inevitable for all embarking passengers.

Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics

Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.

Opportunities and challenges in modeling emerging infectious diseases

The term "pathogen emergence" encompasses everything from previously unidentified viruses entering the human population to established pathogens invading new populations and the evolution of drug resistance. Mathematical models of emergent pathogens allow forecasts of case numbers, investigation of transmission mechanisms, and evaluation of control options. Yet, there are numerous limitations and pitfalls to their use, often driven by data scarcity. Growing availability of data on pathogen genetics and human ecology, coupled with computational and methodological innovations, is amplifying the power of models to inform the public health response to emergence events. Tighter integration of infectious disease models with public health practice and development of resources at the ready has the potential to increase the timeliness and quality of responses.

The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics

BACKGROUND

Malaria remains a problem for many countries classified as malaria free through cases imported from endemic regions. Imported cases to non-endemic countries often result in delays in diagnosis, are expensive to treat, and can sometimes cause secondary local transmission. The movement of malaria in endemic countries has also contributed to the spread of drug resistance and threatens long-term eradication goals. Here we focused on quantifying the international movements of malaria to improve our understanding of these phenomena and facilitate the design of mitigation strategies.

METHODS

In this meta-analysis, we studied the database of publicly available nationally reported statistics on imported malaria in the past 10 years, covering more than 50 000 individual cases. We obtained data from 40 non-endemic countries and recorded the geographical variations.

FINDINGS

Infection movements were strongly skewed towards a small number of high-traffic routes between 2005 and 2015, with the west Africa region accounting for 56% (13 947/24 941) of all imported cases to non-endemic countries with a reported travel destination, and France and the UK receiving the highest number of cases, with more than 4000 reported cases per year on average. Countries strongly linked by movements of imported cases are grouped by historical, language, and travel ties. There is strong spatial clustering of plasmodium species types.

INTERPRETATION

The architecture of the air network, historical ties, demographics of travellers, and malaria endemicity contribute to highly heterogeneous patterns of numbers, routes, and species compositions of parasites transported. With global malaria eradication on the international agenda, malaria control altering local transmission, and the threat of drug resistance, understanding these patterns and their drivers is increasing in importance.

FUNDING

Bill & Melinda Gates Foundation, National Institutes of Health, UK Medical Research Council, UK Department for International Development, Wellcome Trust.

Analysis of Seasonal Risk for Importation of the Mediterranean Fruit Fly, Ceratitis capitata (Diptera: Tephritidae), via Air Passenger Traffic Arriving in Florida and California

The Mediterranean fruit fly, Ceratitis capitata (Wiedemann), is one of the most economically damaging pests in the world and has repeatedly invaded two major agricultural states in the United States, Florida and California, each time requiring costly eradication. The Mediterranean fruit fly gains entry primarily in infested fruit carried by airline passengers and, since Florida and California each receive about 13 million international passengers annually, the risk of Mediterranean fruit fly entering the United States is potentially very high. The risk of passengers bringing the pest into Florida or California from Mediterranean fruit fly-infested countries was determined with two novel models, one estimated seasonal variation in airline passenger number and the other defined the seasonal and spatial variability in Mediterranean fruit fly abundance. These models elucidated relationships among the risk factors for Mediterranean fruit fly introduction, such as amount of passenger traffic, routes traveled, season of travel, abundance of Mediterranean fruit fly in countries where flights departed, and risk of the pest arriving at destination airports. The risk of Mediterranean fruit fly being introduced into Florida was greatest from Colombia, Brazil, Panama, Venezuela, Argentina, and Ecuador during January-August, whereas primarily the risk to California was from Brazil, Panama, Colombia, and Italy in May-August. About three times more Mediterranean fruit flies were intercepted in passenger baggage at airports in Florida than California, although the data were compromised by a lack of systematic sampling and other limitations. Nevertheless, this study achieved the goal of analyzing available data on seasonal passenger flow and Mediterranean fruit fly population levels to determine when surveillance should be intensified at key airports in Florida and California.