A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks.

The COVID-19 pandemic has hit the airline industry hard, leading to heterogeneous epidemiological situations across markets, irregular flight bans, and increasing operational hurdles. Such a melange of irregularities has presented significant challenges to the airline industry, which typically relies on long-term planning. Given the growing risk of disruptions during epidemic and pandemic outbreaks, the role of airline recovery is becoming increasingly crucial for the aviation industry. This study proposes a novel model for airline integrated recovery problem under the risk of in-flight epidemic transmission risks. This model recovers the schedules of aircraft, crew, and passengers to eliminate possible epidemic dissemination while reducing airline operating costs. To account for the high uncertainty with respect to in-flight transmission rates and to prevent overfitting of the empirical distribution, a Wasserstein distance-based ambiguity set is utilized to formulate a distributionally robust optimization model. Aimed at tackling computation difficulties, a branch-and-cut solution method and a large neighborhood search heuristic are proposed in this study based on an epidemic propagation network. The computation results for real-world flight schedules and a probabilistic infection model suggest that the proposed model is capable of reducing the expected number of infected crew members and passengers by 45% with less than 4% increase in flight cancellation/delay rates. Furthermore, practical insights into the selection of critical parameters as well as their relationship with other common disruptions are provided. The integrated model is expected to enhance airline disruption management against major public health events while minimizing economic loss.

On the ramifications of airspace bans in aero-political conflicts: Towards a country importance ranking.

The aviation industry is facing highly volatile developments in the recent years: Following a steady growth phase with prosperous projections, the COVID-19 pandemic has hit the aviation system rather hard. While having gradually entered a recovery process in the years 2021 and 2022, with load factors close to those in the year 2019, the airspace bans between Russia, Europe, and other regions in the world, as part of the ongoing conflict centered around Russia and Ukraine, threaten the orderly operation of flights. This study explores the byproducts and potential impact of airspace bans on the aviation system and its stakeholders, by deriving a ranking of country importance and how they have the potential to influence our aviation system. While being rooted in the analysis of the Russian-Ukrainian conflict, our framework is built up in a generic way and computes an overall country importance metric. We believe that our study contributes to the better understanding of the consequences airspace bans have on our society and the severe needs for effective policies and regulations of such emerging forms of conflict resolution.

STARTUPS: Founding airlines during COVID-19 - A hopeless endeavor or an ample opportunity for a better aviation system?

The devastating impact of COVID-19 on aviation is unprecedented and undoubted in the recent sci-entific literature, with many studies having dissected different facets of COVID-19-induced changes to the industry. A few studies have stepped further and highlighted that the COVID-19 pandemic could have positive long-term impacts on aviation. Given that traditional air carriers are known to be reluctant for performing high-risk experiments outside their business-as-usual, parts of hope for a better aviation future rests on novel players entering the industry. The pandemic - against common perception and odds - might have created a rare opportunity for airline startups to enter the market. In this study, we first dissect the impact of the COVID-19 pandemic on aviation and how it possibly created a breeding ground for new airlines. We propose a framework of eight facets, STARTUPS, covering flight Suspensions, Talents, Aircraft, Recovery, Travel demand, Uniquity, Policy making, and Strategy. Moreover, we analyze the business model and markets of 46 airline startups, established or becoming active during the pandemic. Our study is concluded with a dis-cussion on the risk factors for airline startups during the COVID-19 pandemic and induced policy challenges. Our analysis, we believe, is complementary to existing studies on COVID-19, leveraging a novel perspective on the pandemic and the aviation industry.

Ghostbusters: Hunting abnormal flights in Europe during COVID-19.

The impact of the COVID-19 pandemic is unprecedented in airline history, with irregular flight bans, the inability for accurate demand estimation, several turns in the epidemiological evolution, and a wide range of downstream effects on all aviation stakeholders. While most airlines have increasingly entered a recovery stage, compared to the utmost disruption around April 2020, the airline business is far from back-to-normality. Throughout the past two years, recurrent statements have been made regarding the existence of so-called ghost flights, where airlines operate nearly empty aircraft on markets with insufficient demand, partially with the aim to avoid losing precious airport slots. This study investigates the extent of such abnormal market service during the COVID-19 pandemic through an explorative, data-driven analysis, based on actual load factor data of European airlines for the years 2017 to 2021. We break down the observed deviations by airlines, markets, and airports. We find that low-cost carriers are most-likely to have performed abnormal flights during the pandemic; and that abnormal flights have mostly occurred on frequently-served markets. In addition, we show that airline responses, in terms of departure and yield changes, are largely heterogeneous across the 24 airlines in this study. Our study is the first one to shed light on the important issue of load factor deviations, and we hope that our findings can contribute to a better understanding of the existence of abnormal flights during the pandemic, as well as deriving appropriate policies for dealing with the ubiquitous threat and impact of ghost flights in the future.

Delayed reaction towards emerging COVID-19 variants of concern: Does history repeat itself?

After more than a year with COVID-19, it becomes increasingly clear that certain variants of concern have the potential to be game changers, determining the future of our aviation. These variants pose significant health threats and possibly undermine ongoing vaccination efforts. Recent research showed that flight bans on the initial SARS-CoV-2 outbreak in January 2020 were implemented too late and therefore, turned out to be largely ineffective, enabling a swift turn into a fully-blown pandemic. In this study, we investigate the following question: How effective were existing flight bans against the newly emerged variants of concern? In other words: Do airlines and countries happen to repeat the same mistake again? We analyze the spread of the three most prevalent variants of concern right now: B.1.1.7 (known as the UK variant), B.1.351 (known as the South African variant), and P.1 (known as the Brazilian variant). We find that many countries, again, implemented flights bans once the mutated virus had enough time to be imported via air transportation. To support our empirical analysis further, we designed and implemented a compartmental network spreading model on top of worldwide flight data for the years 2020 and 2021. We observe that the model predictions are rather accurate and confirm our findings. Overall, we hope that our study encourages air transportation stakeholders and policy makers to avoid repeating earlier mistakes in the future, with the ultimate goal to overcome COVID-19 entirely.

TLQP: Early-stage transportation lock-down and quarantine problem.

The advent of COVID-19 is a sensible reminder of the vulnerability of our society to pandemics. We need to be better prepared for finding ways to stem such outbreaks. Except from social distancing and wearing face masks, restricting the movement of people is one important measure necessary to control the spread. Such decisions on the lock-down/reduction of movement should be made in an informed way and, accordingly, modeled as an optimization problem. We propose the Early-stage Transportation Lock-down and Quarantine Problem (TLQP), which can help to decide which parts of the transportation infrastructure of a country should be restricted in early stages. On top of the network-based Susceptible-Exposed-Infectious-Recovered (SEIR) model, we establish a decision recommendation framework, which considers the lock-down of cross-border traffic, internal traffic, and movement inside individual populations. The combinatorial optimization problem aims to find the best set of actions which minimize the social cost of a lock-down. Given the inherent intractability of this problem, we develop a highly-efficient heuristic based on the Effective Distance (ED) path and the Cost-Effective Lazy Forward (CELF) algorithm. We perform and report experiments on the global spread of COVID-19 and show how individual countries may protect their population by taking appropriate measures against the threatening pandemic. We believe that our study contributes to the orchestration of measures for dealing with current and future epidemic outbreaks.

Technological and educational challenges towards pandemic-resilient aviation.

While COVID-19 has devastating effects on aviation, several recent studies have highlighted the potential of the pandemic-induced break for rethinking air transportation, hopefully orchestrating changes towards the construction of a more pandemic-resilient aviation system. Here, pandemic-resilient means that aviation stakeholders can sustain the impact of an epidemic or pandemic outbreak through a more informed reallocation of their resources and more collaborative decision making, while being able to minimize the impacts of external events. Our study contributes to the literature by discussing the challenges associated with technological innovation and education of aviation professionals, on the way towards pandemic-resilient aviation. We discuss issues surrounding technologies for smarter aircraft, smarter airports, and smarter airlines. While technology ensures long-term competitiveness and sustainability, an often-ignored source of challenges are human resources and education. COVID-19 has uncovered and magnified the effects of severe concerns with the current aviation education system, which need to be solved by extended skill sets, modern technology, and better career perspectives. Without properly addressing these technological and educational challenges, the aviation industry likely misses an distinct opportunity for restructuring towards pandemic-resilient aviation.

On the degree of synchronization between air transport connectivity and COVID-19 cases at worldwide level.

COVID-19 is one of the most impactful pandemics in recent history, not only in terms of direct casualties but also regarding socio-economic impact. The goal of our study is to investigate the degree of synchronization between the number of confirmed cases in specific countries, on one hand, and how/at which stage these countries adapted their air transportation operations, on the other hand. We investigate the global air transportation system as a network of countries whose edges represent the existence of direct flights. Aggregated analysis of this country network and its evolving dynamics leads to novel insights regarding the synchronization with the number of confirmed cases; finding that most country borders were likely closed too late. We believe and hope that our analysis leads to a more efficient/effective prevention and control of future epidemics.

Vaccination passports: Challenges for a future of air transportation.

COVID-19 has been a major setback for air transportation; many airlines had to request for bailouts and the international flights connectivity is only restarting slowly. Accordingly, many aviation stakeholders put hopes into the ongoing process of vaccination, with the expectation that a high degree of vaccination will push the envelope for a return to normalcy. One prerequisite for reviving international air connectivity is the introduction of verification documents, also called "vaccination passports". These passports, however, come with several challenges which need to be overcome in order to enable recovery. In this study, we propose a framework covering five important aspects and policy challenges concerning the introduction of vaccination passports for a return of aviation, covering the topics: Competition, Epidemiology, Technology, Ethics, and Politics. Neglecting to appropriately address these challenges will likely not only delay the recovery, but possibly miss an important opportunity before new disastrous events appear on the horizon.

On the contagion leakage via incoming flights during China's aviation policies in the fight against COVID-19.

For nearly three years with the COVID-19 pandemic, China has implemented a set of strict policies to control the flux of potential virus carriers in cross-border flights: The so-called Circuit Breaker mechanism. In this study, we review the evolution of this mechanism - a rather unique experiment in the global aviation system - from a data-driven perspective. Specifically, we perform an investigation on the extent of violations and their potential drivers. In total, 183 events are analyzed covering the period from epidemic outbreak in early 2020 to December 2021. In addition to describing the spatial extent and temporal evolution, we develop a regression model which helps us to better understand the universal patterns. By dissecting an under-investigated phenomenon, we believe that our study contributes to the rich literature on aviation and COVID-19, not only in the specific context of China, but also by assessing some of the challenges and potential of containing a global health threat using strict aviation policies.

A data-driven analysis of the aviation recovery from the COVID-19 pandemic.

In Summer 2022, after a lean COVID-19 spell of almost three years, many airlines reported profits and some airlines even outperformed their pre-pandemic records. In context of the perceived recovery, it is interesting to understand how different markets have gone through the pandemic challenges. In this study, we perform a spatial and temporal dissection of the recovery process the global aviation system went through since May 2020. At the heart of this study, we investigate the patterns underlying market entry decisions during the recovery phase. We identify a rather heterogeneous type of recovery as well as its underlying drivers. We believe that our work is a timely contribution to the research on COVID-19 and aviation, complementary to the existing studies in the literature.

COVID-19 pandemic and air transportation: Summary of Recent Research, Policy Consideration and Future Research Directions.

The COVID-19 pandemic can be considered an unparalleled disruption to the aviation industry in the last century. Starting with an at-that-time inconceivable reduction in the number of flights from March 2020 to May 2020, the aviation industry has been trying to navigate through and out of the crisis. This process is accompanied with a significant number of scientific studies, reporting on the direct and indirect impact of the COVID-19 pandemic on aviation and vice versa. This paper reviews the impacts in context of the recent literature. We have collected nearly 200 well-published papers on the subject in the years 2021/2022 and dissected them into a framework of eight categories, built around: airlines, airports, passengers, workforce, markets, contagion, sustainability, and economics. We highlight the essence of findings in the literature and derive a set of future research directions and policy considerations which we deem important on the way towards pandemic-resilient aviation.

Air transportation as a puzzle piece of COVID-19 in Africa?

COVID-19 has hit our society hard, with more than 242 million cases reported worldwide and more than 4.9 million directly related fatalities. The role of Africa throughout the pandemic has been puzzling, since the African continent seems to have gone through the pandemic better than other continents; clearly better than predicted by the public during the emergence of COVID-19 one year ago. While several factors have been proposed in the literature to explain the unexpected role of Africa, including a relatively young population, more historical-driven preparedness to other types of coronavirus and diseases, and a limited amount of testing, the puzzle is not considered to be solved. In this study, we aim to answer the question whether air transportation indicators can support us in explaining the evolution of COVID-19 in Africa? Using flight data for the year 2020, we explore how changes in the air transportation system correlate with evolution of epidemiological indicators. Our results suggest that air transportation could indeed play a critical role for the spread of COVID-19 in Africa as well. Overall, we hope that our analysis contributes towards a better understanding of COVID-19 and the role air transportation plays in an under-researched region of the world.

COVID-19 pandemic and air transportation: Successfully navigating the paper hurricane.

This paper aims to analyze and understand the impact of the corona virus disease (COVID-19) on aviation and also the role aviation played in the spread of COVID-19, by reviewing the recent scientific literature. We have collected 110 papers on the subject published in the year 2020 and grouped them according to their major application domain, leading to the following categories: Analysis of the global air transportation system during COVID-19, the impacts on the passenger-centric flight experience, and the long-term impacts on broad aviation. Based on the aggregated reported findings in the literature, this paper concludes with a set of recommendations for future scientific directions; hopefully helping aviation to prepare for a post-COVID-19 world.

How did COVID-19 impact air transportation? A first peek through the lens of complex networks.

The current outbreak of COVID-19 is an unprecedented event in air transportation. This is probably the first time that global aviation contributed to the planet-wide spread of a pandemic, with casualties in over two hundred countries. As of August 23rd, 2020, the number of infected cases has topped 23 million, reportedly relating to more than 800,000 deaths worldwide. However, there is also a second side of the pandemic: it has led to an unmatched singularity in the global air transportation system. In what could be considered a highly uncoordinated, almost chaotic manner, countries have closed their borders, and people are reluctant/unable to travel due to country-specific lock-down measures. Accordingly, aviation is one of the industries that has been suffering most due to the consequences of the pandemic outbreak, despite probably being one of its largest initial drivers. In this study, we investigate the impact of COVID-19 on global air transportation at different scales, ranging from worldwide airport networks where airports are nodes and links between airports exist when direct flights exist, to international country networks where countries are contracted as nodes, and to domestic airport networks for representative countries/regions. We focus on the spatial-temporal evolutionary dynamics of COVID-19 in air transportation networks. Our study provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation from a complex system perspective using network science tools.

A comparative analysis of approaches to network-dismantling.

Estimating, understanding, and improving the robustness of networks has many application areas such as bioinformatics, transportation, or computational linguistics. Accordingly, with the rise of network science for modeling complex systems, many methods for robustness estimation and network dismantling have been developed and applied to real-world problems. The state-of-the-art in this field is quite fuzzy, as results are published in various domain-specific venues and using different datasets. In this study, we report, to the best of our knowledge, on the analysis of the largest benchmark regarding network dismantling. We reimplemented and compared 13 competitors on 12 types of random networks, including ER, BA, and WS, with different network generation parameters. We find that network metrics, proposed more than 20 years ago, are often non-dominating competitors, while many recently proposed techniques perform well only on specific network types. Besides the solution quality, we also investigate the execution time. Moreover, we analyze the similarity of competitors, as induced by their node rankings. We compare and validate our results on real-world networks. Our study is aimed to be a reference for selecting a network dismantling method for a given network, considering accuracy requirements and run time constraints.

Sequence Factorization with Multiple References.

The success of high-throughput sequencing has lead to an increasing number of projects which sequence large populations of a species. Storage and analysis of sequence data is a key challenge in these projects, because of the sheer size of the datasets. Compression is one simple technology to deal with this challenge. Referential factorization and compression schemes, which store only the differences between input sequence and a reference sequence, gained lots of interest in this field. Highly-similar sequences, e.g., Human genomes, can be compressed with a compression ratio of 1,000:1 and more, up to two orders of magnitude better than with standard compression techniques. Recently, it was shown that the compression against multiple references from the same species can boost the compression ratio up to 4,000:1. However, a detailed analysis of using multiple references is lacking, e.g., for main memory consumption and optimality. In this paper, we describe one key technique for the referential compression against multiple references: The factorization of sequences. Based on the notion of an optimal factorization, we propose optimization heuristics and identify parameter settings which greatly influence 1) the size of the factorization, 2) the time for factorization, and 3) the required amount of main memory. We evaluate a total of 30 setups with a varying number of references on data from three different species. Our results show a wide range of factorization sizes (optimal to an overhead of up to 300%), factorization speed (0.01 MB/s to more than 600 MB/s), and main memory usage (few dozen MB to dozens of GB). Based on our evaluation, we identify the best configurations for common use cases. Our evaluation shows that multi-reference factorization is much better than single-reference factorization.

On-Demand Indexing for Referential Compression of DNA Sequences.

The decreasing costs of genome sequencing is creating a demand for scalable storage and processing tools and techniques to deal with the large amounts of generated data. Referential compression is one of these techniques, in which the similarity between the DNA of organisms of the same or an evolutionary close species is exploited to reduce the storage demands of genome sequences up to 700 times. The general idea is to store in the compressed file only the differences between the to-be-compressed and a well-known reference sequence. In this paper, we propose a method for improving the performance of referential compression by removing the most costly phase of the process, the complete reference indexing. Our approach, called On-Demand Indexing (ODI) compresses human chromosomes five to ten times faster than other state-of-the-art tools (on average), while achieving similar compression ratios.

FRESCO: Referential compression of highly similar sequences.

In many applications, sets of similar texts or sequences are of high importance. Prominent examples are revision histories of documents or genomic sequences. Modern high-throughput sequencing technologies are able to generate DNA sequences at an ever-increasing rate. In parallel to the decreasing experimental time and cost necessary to produce DNA sequences, computational requirements for analysis and storage of the sequences are steeply increasing. Compression is a key technology to deal with this challenge. Recently, referential compression schemes, storing only the differences between a to-be-compressed input and a known reference sequence, gained a lot of interest in this field. In this paper, we propose a general open-source framework to compress large amounts of biological sequence data called Framework for REferential Sequence COmpression (FRESCO). Our basic compression algorithm is shown to be one to two orders of magnitudes faster than comparable related work, while achieving similar compression ratios. We also propose several techniques to further increase compression ratios, while still retaining the advantage in speed: 1) selecting a good reference sequence; and 2) rewriting a reference sequence to allow for better compression. In addition,we propose a new way of further boosting the compression ratios by applying referential compression to already referentially compressed files (second-order compression). This technique allows for compression ratios way beyond state of the art, for instance,4,000:1 and higher for human genomes. We evaluate our algorithms on a large data set from three different species (more than 1,000 genomes, more than 3 TB) and on a collection of versions of Wikipedia pages. Our results show that real-time compression of highly similar sequences at high compression ratios is possible on modern hardware.

Adaptive efficient compression of genomes.

: Modern high-throughput sequencing technologies are able to generate DNA sequences at an ever increasing rate. In parallel to the decreasing experimental time and cost necessary to produce DNA sequences, computational requirements for analysis and storage of the sequences are steeply increasing. Compression is a key technology to deal with this challenge. Recently, referential compression schemes, storing only the differences between a to-be-compressed input and a known reference sequence, gained a lot of interest in this field. However, memory requirements of the current algorithms are high and run times often are slow. In this paper, we propose an adaptive, parallel and highly efficient referential sequence compression method which allows fine-tuning of the trade-off between required memory and compression speed. When using 12 MB of memory, our method is for human genomes on-par with the best previous algorithms in terms of compression ratio (400:1) and compression speed. In contrast, it compresses a complete human genome in just 11 seconds when provided with 9 GB of main memory, which is almost three times faster than the best competitor while using less main memory.