Evaluating the Impact of the Pandemic Crisis on the Aviation Industry

Abstract

This paper investigates the intellectual structure of the literature addressing “epidemic/pandemic” and “aviation industry” through a bibliometric approach to the literature from 1991 to 2021. The final count of 856 publications was collected from Web of Science and analyzed by CiteSpace (version 5.8.R1) and VOS Viewer. Visualization tools are used to perform the co-citation, co-occurrence, and thematic-based cluster analysis. The results highlight the most prominent nodes (articles, authors, journals, countries, and institutions) within the literature on “epidemic/pandemic” and “aviation industry.” Furthermore, this study conceptualizes and compares the growth of literature before theCOVID-19 pandemic and during the COVID-19 (“hotspot”) era. The conclusion is that the aviation industry is an engine for global economics on the road to recovery from COVID-19, in which soft (human) resources can play an integral part.

In recent years, the world has struggled with a pandemic. The infectious virus was officially named coronavirus disease 2019 (COVID-19) by the World Health Organization on February 11, 2020 ( 1 ). As of December 23, 2021, there had been 276,436,619 confirmed patients of COVID-19, including 5,374,744 deaths ( 2 ). COVID-19 has disrupted societies and economies worldwide, with the aviation sector experiencing one of the most catastrophic impacts ( 3 ). The aviation industry has played an irreplaceable role in rapid mobility for human beings and cargo ( 4 ), but it can be easily affected by the external environment, that is, economic crisis, oil crisis, natural disasters, terrorist attacks, and pandemics ( 5 ). Regulatory institutions implemented various strict control measures, including home quarantines, travel bans, nationwide lockdowns, and border restrictions, to control the spread of COVID-19 ( 6 ), which resulted in a radical decline in the number of air passengers and air services ( 7 ). COVID-19 has had an unprecedented and long-term effect on the aviation industry ( 8 ). Specifically, the International Civil Aviation Organization reported an overall reduction of 2,699 million passengers (−60%) and a USD 371 billion loss of gross passenger operating revenues for airlines in the year 2020 ( 9 ). The International Air Transport Association (IATA) estimated that the recovery of air travel will take several years ( 10 ).

The aviation industry can be regarded as an engine of global economic prosperity because it has made an indispensable contribution to worldwide mobility for populations, international trade, tourism, and education ( 11 ). However, the aviation sector has been deemed a medium for transmission of infectious diseases ( 12 , 13 ). Grout et al. ( 14 ) showed that viruses and parasites could be transmitted globally through international air travel. Stover et al. ( 15 ) argued that emerging infections may spread rapidly through air travel. Chinazzi et al. ( 16 ) also indicated that the aviation sector could expand localized infectious diseases to global pandemics. Our review of the literature revealed the correlation between the aviation industry and various epidemics, such as severe acute respiratory syndrome (SARS) ( 17 ), seasonal influenza ( 18 ), the 2009 H1N1 epidemic (H1N1pdm09 virus) ( 19 ), Ebola virus ( 20 ), dengue fever ( 21 ), Middle East respiratory syndrome (MERS) ( 22 ), and influenza and coronaviruses ( 23 ).

Fever screening has been used to detect febrile individuals at airports ( 24 ). Because COVID-19 can be transmitted through infected patients with pre-symptomatic and asymptomatic conditions ( 25 , 26 ), fever screening is an ineffective measure to identify COVID-19 patients ( 27 ). Moreover, compared with SARS, COVID-19 has a longer incubation period and higher reproductive number. Several studies have indicated that globalization led to COVID-19 spreading faster than SARS and MERS. Wang et al. ( 28 ) also indicated that air travel aggravated the global dissemination of COVID-19. In the IATA annual review of 2020, COVID-19 was reported to be the most challenging scenario for the aviation industry since the end of the Second World War ( 29 ).

Alarmingly, a few COVID-19 incidents, such as the recent outbreak at Nanjing Lukou Airport (Nanjing, China), were caused by an internationally imported case. One member of the air cabin cleaning staff working on both domestic and international flights triggered the infection-transmission chain in China ( 30 ). As a result, it affected 48 cities in 18 provinces, with a total of 1,282 confirmed cases ( 31 ). Similarly, the Delta variant of COVID-19 spread in Australia when a driver was exposed to COVID-19 on June 16, 2021 through the infected cabin crew of a cargo flight ( 32 ). As a result, more than 55,000 individuals ( 33 ) were infected, pushing the Australian aviation industry to a new low and halting the airlines’ recovery ( 34 ).

The previous literature has explored the connection between aviation and the spread of an epidemic. It can be categorized into two distinct phases. First, in the pre-COVID-19 era the literature focused more on the link between the pandemic/epidemic crisis and the aviation industry. The second phase can be called COVID-19 hotspot (2020 onwards), in which increasing numbers of studies have investigated the interaction between aviation and COVID-19. However, most of these studies explored COVID-19 using descriptive or empirical methods, which covered the new lessons learned, realignment of inter-organization complex schemes, and the challenges for organizational resilience.

The future of aviation and its emerging trends can be predicted from the evolution of existing trends. Research related to prospects for recovery of the aviation industry underlined the core methods and potential strategies, which include operational resilience ( 35 ), lessons learned from different success stories ( 36 ), the role of digital transformation in aviation ( 37 ), and key attributes to define resilience and sustainability in the airline industry ( 38 ). Therefore, this study extended the argument by Swanson ( 39 ) that bursts in knowledge evolution leave behind multi-disciplinary gaps that can be captioned as blurred trends in the intellectual growth of the literature.

At the time of writing, new waves of COVID-19 can be labeled as its aftershocks. Particularly, the workforce in transport sectors (especially aviation) have been noted as crucial spots that could cause an outbreak within a short time. Because these staff appear to be potential risk triggers ( 40 ), a comprehensive readiness framework that includes training and education of ground staff ( 41 ) and cabin crew ( 42 ) is required. Therefore, comprehensive behavioral change is required. Although increasing literature on the prospects for recovery of the aviation industry has appeared, it has lacked the underlining human resource aspects. Based on the above-discussed growth and evolution of the literature, this study proposes the following research questions.

• RQ1: What are the key nodes (authors, articles, journals, institutions, and countries) that contribute to the evolution of the literature that addresses pandemic/epidemic challenges in the aviation industry?

• RQ2: Which prominent aspects have been explored in the pre-COVID-19 and COVID-19 hotspot (2020 onward) eras?

• RQ3: What are the possible future research fronts in the light of the ongoing pandemic crisis?

• RQ4: What crucial role does manpower (human resources) play in such infectious challenges?

Specifically, bibliometric research supports underlining the intellectual growth of the discipline in a quantified manner, which can answer the proposed research questions as stated above. It uncovers the emerging trends, patterns, and attributes that bind together to shape the literature ( 43 ). It also visualizes dominating nodes (authors, articles, journals, keywords) ( 44 ). In the existing literature, a few studies have used bibliometric methods to analyze COVID-19 and aviation. For example, Sun et al. ( 45 ) explored the impact of COVID-19 on the aviation industry and the role of air travel in the transmission of COVID-19 by bibliometric analysis. Moreover, Tanrıverdi et al. ( 46 ) draw lessons by conducting a review of existing literature in the Journal of Air Transport Management. However, no research can be recorded as a comprehensive analysis of pandemic/epidemics and the aviation industry. Therefore, this study aims to address the epidemic/pandemic crisis in aviation by examining the intellectual growth of the literature holistically.

Methodology

Data Crawling and Filtration

To address the prime research questions as discussed in the previous section, the authors considered Web of Science (WoS) as the prime source as it can be regarded as one of the repositories with comprehensive details which can be helpful for bibliometric research ( 47 ). We intentionally ignored Scopus as a database because of its vulnerabilities for social sciences (i.e., sociology) and content addressing art and humanities. Moreover, the coverage of WoS goes back to the 1990s, which can help to examine longitudinal data in bibliometrics as compared with Scopus ( 48 ). Conceptually, the search query for the research consists of two conceptual attributes. The first attribute of the search query is comprised of keywords addressing the air transport industry (i.e., air transport, air cargo, aviation, and airport). Moreover, the second half of the search query encapsulated all epidemic/pandemic-related keywords and outbreaks that occurs in recent decades (i.e., infectious diseases, SARS, MERS, Ebola, Zika, and COVID-19).

In the WoS’s Core Collection, the following search query was crawled in the first week of September in the year 2021: TS=(“air transport” OR “air freight” OR “air cargo” OR “air travel” OR “aviation” OR “airport” OR “airline” OR “aircraft”) AND TS=(COVID OR “Infectious disease” OR epidemic OR “sars-cov-2” OR pandemic OR coronavirus OR influenza OR virus OR h1n1 OR Zika OR Ebola OR MERS OR “Middle East respiratory syndrome” OR “2019-ncov” OR SARS OR “Severe Acute Respiratory Syndrome”). The search query is particularly designed to include all content that can address the aviation and air transport industry in the time of pandemic or epidemic crisis in the years from 1991 to 2021.

As per the guidance by PRISMA ( 49 ), initially 1,614 research records were identified via WoS (as the preferred database). After excluding 272 documents which included conference papers, reviews, book chapters and letters, 1,342 were taken forward for the screening process. The remaining record count was explored further based on titles, keywords, abstracts, and the year of publication, as a part of the screening phase. After excluding 274 records, 1,068 records were selected for retrieval. As 136 records failed to be retrieved, the remaining 932 records were refined further by revisiting the record to inspect their full text in social sciences, science and art, and humanities indexing. After excluding 76 records which proved to be irrelevant on reading the complete article, the final count of 856 records were used in the bibliometric research. A graphical explanation of the data collection and the crawling process is shown in Figure 1.

Figure 1.

PRISMA flow diagram.

Tools and Techniques

Bibliometric analysis can be performed on huge amounts of scientific data to uncover the evolutionary attributes, patterns, and trends within a specific research field by underlining the relationships among nodes (articles, authors, journals, institutions, and countries) through social network analysis of the intellectual structure of any knowledge domain. For instance, co-citation helps to underline prominent nodes that have greater influence in the research area. It highlights the nodes’ relatedness and potential significance in the concerned knowledge area ( 50 ). Chen ( 51 ) argued that co-citation helps to underline the relevance of citation and similarity between two nodes based on their content and cognitive proximity. It also helps to explore co-citation-based thematic cluster analysis ( 52 ).

Any node with a high co-citation count within any thematic cluster can also potentially hold a greater degree of centrality. Nodes with high centrality act as evolutionary nodes (turning point) to connect thematic clusters (intellectual base) within the research area ( 53 ). In the social network analysis, Kleinberg’s ( 54 ) “burst detection” algorithm can also be used, because it can help to identify the most active nodes in specific research domains by observing a sudden surge of citations. Generally, one or more high citation burst nodes also help in creating and directing turning points in the research domain. Moreover, in social network analysis, co-occurrence helps to examine the relationship between the nodes (i.e., keywords, countries, and institutions) while defining the macrostructure of the knowledge map within a certain research area.

To perform the bibliometric features discussed above (co-citation, co-occurrence, and thematic cluster analysis), the authors used CiteSpace (version 5.8. R1) and VOSViewer. CiteSpace provides a java-based environment to visualize literature in bibliometric studies and offers a built-in function to interpret and convert crawled data from academic databases (i.e., WoS and Scopus) into a correlation matrix to explore the intellectual structure in bibliometric studies. In particular, CiteSpace is used because it helps to eliminate duplications in the data pre-processing, offers network extraction algorithms (pathfinder and minimum spanning tree), and provides co-citation/co-occurrence based cluster view, timeline, and burst detection mechanism for visualizing data ( 55 ). Moreover, to examine the collaboration network of authors and institutions, VOS Viewer is used because it provides easy Graphical User Interface (GUI) based interaction for mapping visual maps in bibliometric research. Several academic research initiatives have adopted VOS Viewer before, which signifies its reliability and credibility for the current research purpose ( 56 ).

Findings

RQ1: Descriptive and Prominent Nodes

The crawled data comprised 856 research articles and 20,736 cited references for the last 30 years (1991 to 2021). The literature comprised contributions from 92 countries, 641 funding agencies, 1,557 institutions, 394 academic journals, and 4,200 authors. Furthermore, 10,270 references were made by 395 authors in 169 journal articles published in the literature that address pandemic/epidemic issues and aviation. Noticeably, Journal of Air Transport Management, Transport Policy, and PLoS One were recorded as the most prominent content contributors. From a regional perspective, the United States, the United Kingdom (UK), and China were recorded as the dominant contributors in the literature. Interestingly, publications published in the last three years of the period accounted for more than 51% of the whole literature, which also signifies the need for this study. A graphical view of the publications and citations count is presented in Figure 2.

Figure 2.

Total publication and citation count in the literature on “pandemic/epidemic” and “aviation industry.”

Article Co-Citation Analyses

During the co-citation analysis of the crawled data from WoS, the authors observed 1,067 nodes with their co-citation scores by using a minimum spanning tree. In the aviation and pandemic/epidemic research, the most co-cited article was, “The Effect of Travel Restrictions on the Spread of the 2019 Novel Coronavirus (COVID-19) Outbreak” by Chinazzi et al. ( 16 ). The article emphasized the effects of national and international travel restrictions (as a policy) in a time of pandemic crisis by adopting a global metapopulation disease transmission model. The article with the second highest citation count was that by Suau-Sanchez et al. ( 3 ) with the title “An Early Assessment of the Impact of COVID-19 on Air Transport: Just Another Crisis or the end of Aviation as we Know it?”. It underlined the potential long-term effects of the COVID-19 pandemic on the commercial aviation industry in a qualitative manner. The third most highly co-cited reference was that by Bogoch et al. ( 20 ) with the title, “Assessment of the Potential for International Dissemination of Ebola Virus via Commercial Air Travel During the 2014 West African Outbreak.” The authors emphasized that screening international travelers departing from airports would be an effective tool in controling epidemic crises (in this case, Ebola). The fourth most highly co-cited reference was that by Cooper et al. ( 57 ) with the title, “Delaying the International Spread of Pandemic Influenza.” The paper developed stochastic models of the international spread of influenza to assess the potential of local control measures and travel restrictions for impeding global dissemination. The fifth most highly co-cited article was, “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia” by Li et al. ( 58 ). The authors described the characteristics of COVID-19 patients and their key epidemiologic time-delay distributions. Noticeably, this can be recorded as among the first publications to confirm human-to-human transmission of COVID-19.

The sixth most highly co-cited article was, “Spread of a Novel Influenza A (H1N1) Virus via Global Airline Transportation” by Khan et al. ( 18 ), which suggested that quantitative analysis of global air-traffic patterns can effectively predict and map the risks of importing global infectious diseases. The seventh most highly co-cited article was, “Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings” by Fraser et al. ( 59 ). The paper emphasized the value of epidemiological analysis and modeling, which is based on high-quality epidemiological data collected by public health agencies around the world. The eighth most highly co-cited article was, “Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions” by Colizza et al. ( 60 ), which concluded that air transportation should be included in the evaluation of the occurrence probability of a pandemic. The ninth most highly co-cited paper was that by Kraemer et al., with the title, “The Effect of Human Mobility and Control Measures on the COVID-19 Epidemic in China.” The finding of that research is that strict travel restrictions can effectively reduce the transmission of infectious disease in the early phase of the outbreak, while such measures could be ineffective in the latter stages of the pandemic ( 61 ). The tenth most highly co-cited article was, “Clinical Features of Patients Infected With 2019 Novel Coronavirus in Wuhan, China” by Huang et al. ( 62 ). That paper is a prospective analysis of clinical features of patients infected with COVID-19, and the authors stated the need for multi-dimensional, open-ended research to address the origin, epidemiology, duration of human transmission, and clinical spectrum of COVID-19. Noticeably, the article was published online on January 24, 2020, which was only three days after the Chinese government announced COVID-19 as an epidemic crisis.

Co-Citation Analysis of Authors and Journals

Author co-citation analysis underlines the interrelationships among scholars and highlights the significance of the highlight-cited authors within the knowledge domain ( 63 ). In this research, the primary data recorded 813 nodes (authors) and 3,810 links between the years 1991 and 2021. Among the most highly co-cited authors are Alexandra Mangili with 71, Vittoria Colizza with 58, and Annelies Wilder-Smith with 54. Furthermore, for centrality count, Kamran Khan had a centrality score of .13, while Issac Bogosch had a centrality score of .11, and these were noted as prominent contributors that defined new research directions within the intellectual structure of “pandemic/epidemic” and “aviation industry.” Lastly, according to the authors’ collaboration network analysis, Kamran Khan, Nicki Persik, Issac Bogosch, Vittoria Colizza, and Annelies Wilder-Smith were active nodes in the authors’ collaboration networking, as shown in Figure 3.

Figure 3.

Authors’ collaboration network in the intellectual structure of literature on the aviation industry and pandemic/epidemic crisis.

Journal co-citation network analysis can recognize common points and examine the structure of the relationship across subjects within journals ( 64 ). It is often used to explore the macrostructure of a research area and identify the most influential journals, which can provide insights into the intellectual foundation of a discipline ( 63 ). The findings of this study showed that the prominent journals that contributed to the intellectual foundation of “pandemic/epidemic” and “aviation industry” research were Journal of Air Transport Management, PLoS One, Journal of Travel Medicine, Emerging Infectious Disease, Aviation Space and Environmental Medicine, and Transport Policy. The cumulative contribution of these journals accounted for around 50% of contribution in the journals’ co-citation analysis.

Countries and Institutions Co-Occurrence Analysis

In this section, the most influential and contributing countries and institutions in the research on “pandemic/epidemic” and “aviation industry” are discussed. The network of country co-occurrence analysis is comprised of 92 nodes and 273 links between the years 1991 and 2021. The results indicated the dominant roles of the United States, the UK, China, Australia, and Germany. For the country-level collaboration network, the United States, the UK, and China are highly active. Moreover, the burst detection in the country-level co-occurrence analysis highlighted the names of several European countries, namely, France (occurrence began in 1996 and ended in 2008, with a strength count of 6.44), Italy (began in 2007 and ended in 2014, with the strength count of 3.7), and Sweden (from 2014 to 2018, with the strength count 4.68).

The institutions co-occurrence network consisted of 501 nodes and 705 links between 1991 and 2021. The most noticeable nodes (institutions) are listed as: U.S. Centers for Disease Control and Prevention, University of Toronto (Canada), St Michael’s Hospital (Canada), University of Oxford (UK), and University of Sydney (Australia). Moreover, the burst detection within the institutions co-citation analysis showed that St Michael’s Hospital had the strongest citation burst (strength count of 4.85), which began in 2015 and ended in 2019. The second position is London School of Hygiene & Tropical Medicine (from 2016 to 2021), followed by Hong Kong Polytechnic University (from 2019 to 2021). While examining the hotspots in the institutional collaboration network analysis, Indiana University (U.S.A.), University of Toronto, University of Hong Kong, and University of Oxford were also dominant, as shown in Figure 4.

Figure 4.

Hotspot analysis in case of institutional collaboration networking: (a) option 1 and (b) option 2.

RQ2: Pre-COVID and COVID-19 Eras in Comparison

During the co-citation-based cluster analysis, as defined in Methodology section, the thematic sub-classification within the intellectual structure of the knowledge domain was explored. This study observed 13 noticeable clusters within the knowledge domain of pandemic/epidemic in the aviation industry. However, we chose to emphasize the six most dominant clusters in the pre-COVID-19 and COVID-19 (hotspot) eras. The graphical view of the cluster view of co-cited references is shown in Figure 5. The overall modularity, weighted mean silhouette, and harmonic mean satisfied the lower cut-off limits as suggested by Chen ( 65 ).

Figure 5.

Cluster view of “epidemic/pandemic” and “aviation industry” (as research area).

Clusters From Pre-COVID-19 Era

Among the dominant clusters from the pre-COVID-19 era, the cluster “Emerging Pathogen” (silhouette score = 0.966) can be labeled as the most prominent. Specifically, the nodes within this cluster emphasized the role of the natural history of any specific infectious disease ( 66 ) and the value of understanding pathogen evolution for outbreak surveillance ( 67 ). The second prominent cluster “Pandemic Influenza” (silhouette score = 0.963) focused on the effects of air travel and its potential relatedness to the global transmission of pandemic/epidemic ( 68 ). Specifically, the cluster emphasized the related challenges faced during mass gatherings (i.e., religious gatherings in Macca, Kingdom of Saudi Arabia) ( 69 ). The third most visible cluster was “Global Epidemics” (silhouette score = 0.945). Most of the articles in this cluster underlined the effect of restrictions on air travel on controling pandemics/epidemics ( 70 ) and highlighted the value of stochastic models for predicting the global epidemic ( 71 ). In the broader view of the pre-COVID-19 clusters, articles addressed the previously witnessed outbreaks (Ebola, H1N1, and MERS) and the most prominent ones attempted to quantify the model-based research to understand the spread of any infectious disease.

Clusters From COVID-19 (Hotspot) Era

In the COVID-19 (hotspot) era, the most prominent cluster observed was “COVID-19 spread” (silhouette score = .997). Papers in this cluster included the assessment of the relationship between transportation networks and COVID-19 transmission ( 72 ) and highlighted the effect of public health intervention in controlling COVID-19 ( 73 ). The second most visible cluster was “Social Distancing” (silhouette score of .990), which specifically explored the possible scenarios that implement health-protective measures in the aviation industry. Among them, most health-protective measuring practices have revolved around the value of social distancing in preventing COVID-19, that is, effective measures in boarding ( 74 ) and seating assignments ( 45 ). The third most highlighted cluster was “Air Travel” (silhouette score = .984), which underlined the effects of COVID-19 on the air transport industry. For instance, Warnock-Smith et al. ( 75 ) explored the Chinese air transport market during the pandemic crisis. Cui et al. ( 76 ) analyzed China’s transport sectors by using Computable General Equilibrium (CGE) model coupled with a decomposition analysis approach.

RQ3: Potential Future Research Fronts

To underline the prominent trends and future fronts, this study adopted the approach recommended by Chen ( 65 ), which helps to identify: (i) the emerging trends by measuring the degree of centrality, (ii) the ongoing burst count, and (iii) noticeable clusters in the recent slice of time.

Noticeable Turning Points in Article Co-Citation Analysis

In the article co-citation analysis, a higher score of centrality underlines the significant nodes, which can define and direct new dimensions within the intellectual structure of any research domain ( 52 ). In the literature related to “epidemic/pandemic” and “aviation industry”, five distinctive nodes with high centrality count scores were observed. In detail, (i) Zhang and Chen ( 77 ) (centrality score = 15) predicted particle concentration distributions in enclosed spaces by using the Eulerian and Lagrangian methods. (ii) Longini et al. ( 78 ) (centrality score = 14) simulated highly pathogenic avian influenza A (subtype H5N1) by using a stochastic influenza simulation model. (iii) Pastor-Satorras and Vespignani ( 79 ) (centrality score = 14) examined the epidemic spreading in scale-free networks. (iv) Cooper et al. ( 57 ) (centrality score = 10) analyzed the international spread of influenza by using stochastic models. (v) Bogoch et al. ( 20 ) (centrality score = 9) underlined the effectiveness of air travel restrictions and the importance of airport-based traveler screening. In summary, using the article co-citation analysis, the nodes with high centrality scores showed that the research on “epidemic/pandemic” and “aviation industry” has expanded the direction of the noticeable nodes from the pre-COVID era, which emphasized the role of the model or simulation for the assessment of a pandemic/epidemic.

Ongoing Bursts in Keyword Co-Occurrence Analysis

The findings of keyword co-occurrence analysis were that “COVID-19” is the keyword with the highest burst count (32.61), as shown in Table 1. In this section, the dominating attributes within “COVID-19” as a keyword will be explored and discussed by using “Node detail” in CiteSpace.

Table 1.

Keywords-Based Burst Analysis

Keyword	Burst size	Begin	End	Span
Outbreak	4.11	2010	2016
Ebola	4.07	2015	2019
Transmission	3.98	2015	2017
Zika virus	3.44	2016	2019
Travel	4.18	2018	2019
Infectious disease	2.72	2018	2019
COVID-19	32.61	2020	2022
Aviation	3.29	2020	2022

Note: Bold years indicate the beginning of the keyword's burst.

In the case of “COVID-19,” the prominent attributes included the examination of the aircraft cabin and several modeling techniques that have been proposed in recent years. Additionally, the cross-countries pandemic connectedness has been a great challenge for researchers, policymakers, and the aviation industry ( 35 ). Because of the development of technology, spatial-temporal data analysis techniques have been widely used ( 16 ). However, the COVID-19 pandemic, which started in the first quarter of 2020, has persisted and evolved worldwide, thereby affecting passengers of both international and national flights. Although some methods, such as the contact tracing method, can be regarded as effective approaches to mitigate the spread of disease in the initial stage of an outbreak ( 80 ), no effective way can completely resolve the transmission of a disease.

Predicting Future Cluster (Aircraft Cabin)

The aircraft cabin is a relatively enclosed environment that passengers cannot leave during the entire trip. Human-exhaled infectious pathogens remain at high levels in the cabin because of the low per-person ventilation rate. Compared with other modes of transportation, the air cabin has a higher occupant density and longer exposure time, which could cause a high occurrence probability of the spread of disease through airborne and contact modes. In the pre-COVID-19 era, the research related to the relatedness of infectious disease and the cabin environment often recommended mathematical modeling to explore disease transmission. Typical examples include the following research work. Mangili and Gendreau ( 81 ) reviewed numerous outbreaks of respiratory diseases among passengers in aircraft and suggested that mathematical modeling can be a useful tool for risk assessment of disease transmission within the aircraft cabin. Another approach to predicting infection risk is the study that considered the characteristics of the exhalation of the droplets carrying infectious agents from index passengers by Gupta et al. ( 82 ).

However, COVID-19 can be shed and transmitted via asymptomatic and symptomatic infected persons, that is, the routes of COVID-19 transmission include aerosol and fomite ( 83 ). COVID-19 patients can also release the virus into the surrounding air through their breath, which can survive for a long time and be transported for a long distance ( 84 ). Although most aircraft cabins have ventilation and air filtration systems, air passengers seated within two rows still have a high infection risk of COVID-19 ( 6 ). Furthermore, COVID-19 can remain infectious on hard surfaces from hours to days ( 85 ). Several studies have indicated that high transmission probability of COVID-19 occurs under some special conditions that include high occupant density, enclosed environment, and high frequency of contacts. The same viewpoint by Cevik et al. ( 86 ) also presented that enclosed and crowded environments could potentially increase the transmission of COVID-19.

RQ4: Manpower

According to the findings related to RQ1-3, in the field of “epidemic/pandemic” and “aviation industry,” the main focuses of academics and policymakers were on the economic impact or stakeholders’ management through simulation or modeling. However, as discussed in the first section of this paper, recent outbreaks in Australia and China serve as a reminder to revisit the current practices of the aviation industry. Therefore, to examine the holistic views and underline the soft aspect (human resources), this study explored the role of manpower within the literature on “pandemic/epidemic” and “aviation industry” as the fourth research question.

In-Flight/Cabin Crew

The article co-citation analysis based on the cluster view of the literature on “pandemic/epidemic” and “aviation industry” showed that the “soft role” (human aspects) was rarely observed as a noticeable node in the literature. With regard to “cabin crew,” the visible nodes include the work by Görlich and Stadelmann ( 87 ) who studied the mental health of in-flight cabin crew in the pre-COVID-19 and COVID-19 eras. Toprani et al. ( 88 ) underlined the possible risk of COVID-19 for flight crew and suggested that the collaborative mechanism of all active and passive stakeholders can reduce the risk. Moreover, Zheng et al. ( 89 ) mentioned the relatedness of external transportation and logistics networks with airports during the COVID-19 pandemic.

Based on insights from existing literature studied in the current research, further studies can include initiatives to analyze the case study-based research on flight attendants who experience difficulties because of COVID-19. Moreover, studies can examine and propose the criteria to evaluate cabin crew health and human resources’ fitness-to-fly in the case of a recent pandemic crisis. Furthermore, future studies can explore the role of professional training and enhancement of infection prevention awareness to effectively prevent COVID-19 from spreading during flying. The reason to emphasize the significance of cabin crew is that they can appear in many locations, such as airline operating areas, civil aviation authorities, hospitality industry, logistics and transportation, and local stopover areas, and encounter passengers at the airport and in-flight, which poses a higher degree of potential risks to spread infectious disease.

Ground Staff

The keyword co-occurrence analysis of the literature on “pandemic/epidemic” and “aviation industry” represented that the health problems of ground staff and airport employees have not attracted enough interest from researchers. Airport staff who could be in close contact with passengers from the epicenter of the outbreak have a high degree of fear ( 90 ). Malagón-Rojas et al. ( 40 ) used mixed methods to explore airport workers’ infection and risk perception of COVID-19 and concluded that work conditions and environment significantly affect airport workers’ infection and risk perception. The usage of personal protective equipment and the disinfection of potentially contaminated areas can effectively mitigate the risk perception of workers. De Rooij et al. ( 41 ) evaluated training demand in infectious disease management at points of entry, including major ports, airports, and ground-crossing in Europe. The findings reported that European countries should collaborate on infectious disease management and build a trained and prepared workforce to deal with the next epidemic.

Discussion

According to the authors’ co-citation analysis (as a part of RQ1), prominent contributors in the pre-COVID era and the implications related to a previously occurring pandemic or epidemic incidences, that is, SARS (17), Ebola virus (20), dengue fever (21), and MERS (22), were recorded and were considered to automatically lead to the future prediction. The prominent journals focused on the areas of public environmental occupational health, infectious diseases, transportation, general medicine, internal medicine, immunology, and environmental science. However, the journals that specifically address the economic and multi-disciplinary aspects are still limited in that they cannot make a prominent contribution. Moreover, none of the prominent positions in the case of country- and institution-level analysis was secured by any of the developing economies. The gap in the contribution from the developing and developed regions can predict the future challenge of skewness in the research findings. Therefore, it could pose a challenge for any global or generalized solution in the concerned research area.

From the holistic clustered view of co-citation analysis, as performed while answering RQ2, knowledge fronts covered the nature and history of specific infectious diseases, the relatedness of the aviation industry to the epidemic/pandemic crisis, and the effect of policy-level control that imposed travel restrictions in the pre-COVID-19 era. However, the COVID-19 pandemic was observed to be more challenging because of its unpredictable, ambiguous, and uncertain nature. First, compared with SARS, COVID-19 has a higher reproductive rate ( 91 ) and longer incubation period ( 92 ). Specifically, the average incubation period is 5.2 days and in some scenarios it can extend to more than 12 days ( 93 ). Moreover, because COVID-19 can be shed and transmitted via asymptomatic and pre-symptomatic infected persons ( 25 ), fever screening (a major measure used to detect infected individuals in previous pandemics) is inefficient to identify the COVID-19 infectors ( 27 ). Second, globalization caused COVID-19 to spread faster, and the role of media and rumors make the social setting more complex, which is more challenging for regulatory institutions. However, studies on the role of social distancing and the social impact of the pandemic crisis were limited during the pre-COVID-19 era. Based on the article co-citation-based cluster view, this study can conclude that the societal role, economic impact, and multi-disciplinary aspects would be the emerging trends that can bridge the gap between the pre-COVID-19 and COVID-19 (hotspot) eras to drive the future direction of the literature.

In accordance with RQ3, the findings indicated that the most noticeable nodes (in degree of centrality) underlined the function of the modeling and simulation approach for the pandemic crisis.

In line with RQ3, most of the noticeable nodes for the degree of centrality underlined the role of modeling and simulation. In the pre-COVID-19 era, the role of behavior modeling was emphasized, that is, risk perception ( 94 ). However, in the COVID-19 (hotspot) era, the topics of: pandemic induced behavioral change, lifestyle intervention-based health behaviors, and predictions related to passengers traveling after COVID-19 have already been under discussion in academia. Based on such emerging trends, this study predicts that behavioral modeling will be a new knowledge front in the literature related to the “pandemic/epidemic” and “aviation industry.” In particular, socio-psychological factors will play a prominent part of the contributing knowledge. Furthermore, from the keywords co-occurrence analysis, the study highlighted that the modeling studies, epidemiological analysis, spatial-temporal databases, air travel pattern, spatial spread, flights passengers flow, government support, and contact tracing practices have been discussed intensively in the literature on “pandemic/epidemic” and “aviation industry.” This study also found that some research aspects, including emerging business model, airport privatization, airport operating procedure, considering social inclusion, successful containment, and technology-support distancing, have been investigated less, but are predicted to be the dominant ones in the future.

The aviation industry is comprised of various stakeholders who may belong to different sectors, departments, organizations, or groups, such as different airports, airlines, and service outsourcing companies. In the COVID-19 era, the arguments and research have switched to the arrangement and boarding, and human-to-human interaction on the ground and in-flight, which have also posed a greater challenge for governments, administrative authorities of airports and terminals, on the ground, in-flight staff, and passengers. From the perspective of total quality management, the supply chain of the aviation industry is not restricted to passengers and staff. Instead, it is composed of other related business entities, which have been directly or indirectly affected by the aviation industry (i.e., the transportation network, labor dispatch industry, and hospitality industry). In other words, the services offered to customers by the aviation industry range from booking tickets to arriving at the destination, in which anyone linked in the whole supply chain affects customer experience and satisfaction. Therefore, this study concludes that cost optimization, privatization, and outsourcing will be among the greatest challenges for the supply-side of the aviation industry. Many stakeholders, including public and private airlines, third-party ground operations staff, security agencies, and outsourcing companies must coordinate effectively. Meanwhile, ethical traveling behaviors and restricted health-conscious behaviors of travelers can be predicted as the critical parts of the literature related to epidemics/pandemics and the aviation industry.

To address RQ4, Porter’s value chain network can be considered to underline the significance of manpower. The literature connected with the aviation industry has discussed firm infrastructure, technology development, logistics, operation, marketing, and services, while human resource management content has been the least strategic in defining and designing revived plans for pandemic or epidemic crises. In the pre-COVID-19 era, several national and international airlines had an alliance to share resources to realize synergism in the aviation industry. Typical alliances include Star Alliance, SkyTeam, and OneWorld, which not only provided value to airlines (i.e., by code-sharing, maintaining the facility, operation equipment, and staff), but also offer services to potential consumers with economic or time benefits (i.e., low fare, flexible departures, and connecting flights) ( 95 ). Recently, to cope with the COVID-19 pandemic, three dominant alliances (Star Alliance, SkyTeam, and OneWorld) jointly made a call expressing their hope that governments and stakeholders worldwide would sincerely cooperate to help the global aviation industry ( 96 ).

Furthermore, from the macro-level perspective, economic, legal, and ecological concerns have been emphasized by the industry, while a few studies have explored the regulative and social factors. Abate et al. ( 97 ) argued that balancing between connectivity and competition is a huge challenge that involves different political and economic dimensions. Tisdall et al. ( 7 ) indicated that previous policymakers lacked practical experience and suggested that the policies in the aviation industry should be crafted by using the multi-disciplinary view. Lastly, the aviation industry also involves or cooperates with communities, external transportation networks, and extended supply-chain networks that include civil aviation authority, customs and immigration staff, freight handling crew, and health workers. Studies that performed a comprehensive analysis have noticeably ignored pandemics/epidemics and the aviation industry. Some research clusters, such as the history of influenza, modeling, and simulation, and the new normal of social distancing demand, should be integrated as holistic health-protective measures that could guide stakeholders to make active efforts for the recovery of the aviation industry. As the transportation sector (which includes the aviation industry) is not an isolated one, it actively and passively connects with several other related industries which include tourism, education, international trade and commerce. Thus, it provides valuable a lead to address common challenges such as social distancing. Besides this, as the study predicts, future research may address issues related to aircraft cabins and the role of manpower in the aviation industry in the context of a pandemic crisis. The study implies that the innovation in close spaces (including aircraft cabin) will be reviewed and improved by engineering and research to address the severe challenges of airborne infectious diseases.

Future Direction and Conclusion

This study offered a detailed review of the literature related to the aviation industry and pandemic/epidemic crises. It used bibliometric tools and techniques to explore or resolve the following trends: (RQ1) Highlight the prominent articles, authors, and journals by using co-citation and conduct countries and institution-level analysis using co-occurrence analysis. (RQ2) Prominent research articles in the pre- and during the COVID-19 (hotspot) era were recorded by performing articles co-citation-based cluster analysis. (RQ3) Predicted the future trends in the academic literature by examining the centrality count of the article co-citation analysis, recent burst in the keywords by conducting co-occurrence analysis, and emerging cluster from the articles co-citation-based cluster view. (RQ4) Underline the role of manpower in the literature by using keywords’ co-occurrence analysis.

The findings indicate that the qualitative, behavioral, and belief modeling, and multi-disciplinary view that understands the stakeholders’ perceptions toward the epidemic/pandemic crisis in the aviation industry still has a gap, and the need for soft (human) resources can be emphasized in holistic manner. In future research, the case study of an airport (terminal) eco-system can explored to identify the strategic role of soft (human) resources to deal with a pandemic or epidemic crisis. Moreover, the psychological stress, individual performance, or burnout among different organizations that work actively or passively in or for the aviation industry can be investigated to examine the role of soft (human) resources in the aviation industry. Furthermore, by using the bibliometric approach, the role of “human resources” and “aviation industry” during the COVID-19 pandemic can be examined to revisit the strategic role of soft resources in the aviation industry.