Descriptive Assessment of Big Data Technologies Application in the Prevention of Infectious Diseases’ Spread through Air Travels

Introduction

The spread of infectious diseases through air travels remains a significant global threat because of the profound impacts on health, economies, education and socio-politics [1,2]. Historically, infectious disease outbreaks have caused the highest mortality rates compared to other disaster types and often arise as secondary consequences of natural disasters such as earthquakes, floods, droughts, wars, tsunamis, and heat waves or human-made events conflicts, dam collapse and bio-terrorism acts [3-5]. The widespread international movement of people facilitated by the aviation industry has increasingly contributed to the rapid global spread of infectious diseases [6,7]. The rapid air travel events have intensified the challenges of disease control and prevention [8]. Several notable infectious disease outbreaks have demonstrated the critical role of international air travel in their spread. For instance, the H1N1 influenza pandemic of 2009 began in Mexico and quickly escalated into a global health emergency within months [9]. Similarly, the Ebola virus epidemic in West Africa (2013– 2016) and the Zika virus outbreak in 2015–2016 exhibited rapid geographic expansion beyond their points of origin, amplified by international travel [10,11]. Each of these infectious disease crises was declared a public health emergency of international concern (PHEIC) to show the urgency of effective global surveillance and containment measures [12].

The health consequences of infectious disease outbreaks are matched often by enormous economic burdens [13]. The Middle East Respiratory Syndrome (MERS) outbreak in 2012, for example, affected 2,578 people with 888 recorded deaths and incurred hospital management costs averaging $12,947 per patient [14,15]. The 2014 Ebola outbreak resulted in nearly 10,000 deaths and imposed multibillion-dollar economic losses with projections estimating costs reaching hundreds of billions if uncontrolled global spread occurred [16]. The COVID-19 pandemic that infected over 258 million people and causing more than 5 million deaths worldwide as of late 2021 further illustrated this dual burden of infectious diseases [17,18]. It inflicted trillions of dollars economic damage globally and cause unprecedented strain on health systems [19]. In light of these challenges, Precision Public Health (PPH) has emerged as a multidisciplinary approach that leverages big data and advanced analytics to enhance infectious disease surveillance, prediction and response at population level [20,21]. Roberts et al. argued that unlike precision medicine, which focuses on individual genomic information, PPH utilizes diverse data sources from environmental, social and health domains to deliver targeted public health interventions [22]. The application of big data, a technology characterized by its volume, velocity, variety, variability and veracity, enables real-time monitoring and evidence-based decision-making to improve outbreak management [21,23].

Technological advancements have supported the implementation of big data in infectious disease control [24]. Open-source frameworks such as Apache Hadoop and cloud computing environments provide scalable infrastructure to handle massive health datasets generated from international travel screening, remote monitoring systems, and healthcare facility records [21,25,26]. These big data tools facilitate and enhance surveillance, rapid case detection, outbreak prediction and risk communication that are critical for responding to dynamic and complex disease transmission patterns [25]. Given the intersection between air travel and infectious disease spread, and the fact that international airports serve as pivotal entry points, it is important to execute big data-driven surveillance and control strategies at the airports [27]. Ajodo et al., Vizitiu et al. and Batista had argued that implementing advanced analytics and real-time monitoring at airport checkpoints help in early identification of suspected cases, which enable prompt isolation and reducing disease transmission across borders [28-30]. This study examines the role and effectiveness of big data tools in managing infectious disease risks through three selected international airports in Nigerian. It aligned with global health protocols and the strategic public health priorities advocated by WHO and other international bodies. This research contributes to the growing field of PPH and offers evidence to guide policy and operational improvements by exploring the adoption and integration of big data technologies in airports for health security systems. In doing so, it addresses the broader goal of minimizing both the human and economic costs associated with infectious diseases transmitted via air travel. It supports the broader efforts toward building resilient and adaptive global health defense mechanisms against infectious diseases outbreaks and their spread at a global scale.

Research Methodology

The study adopted descriptive quantitative research approach that used an online questionnaire to collect data from participants working within the Nigerian airports and aviation sectors. The quantitative online survey approach was chosen for its ability to efficiently gather data from a large and diverse sample within a short period. It is equally cost-effective and useful for simultaneous data collection from multiple participants from different airports [31]. The use of a structured questionnaire facilitated the collection of quantifiable data that was analyzed statistically to draw inferences on the adoption and application of big data tools in preventing infectious diseases’ spread through international air travel. Participants were selected from staff employed at purposively chosen international airports in Nigeria. The inclusion criteria required participants to be actively working within these airports’ operational sectors. This inclusion criterion ensured relevance and validity of responses to the research aim and objectives [32].

The online questionnaire was structured into three main sections: the first section prescreened participants for informed consent and involvement in airport operations to ensure only participants who gave their consent and works in one of the selected airports as at the time of the study were eligible to participate. Ineligible participants were screened out automatically at this stage of the survey. The second section gathered demographic information to profile the respondents, while the third section employed primarily a 5-point Likert scale to quantify perceptions and adoption levels of various infectious disease prevention big data tools and applications. Responses on the Likert scale ranged from 5 (highly applied) to 1 (not applied). The Likert Scale points was supplemented by 'yes,' 'not sure,' and 'no,' assigned numeric values for quantitative analysis. This design enabled collection of data on the practical deployment and effectiveness of big data tools in diseases prevention efforts.

The questionnaire was administered online through Google Open Data Kit (ODK) forms, which facilitated easy distribution, access and completion by participants actively involved in the airport operations. This online delivery method improved data collection efficiency [31]. It allowed respondents to participate remotely and at their convenience, while also supporting rapid data aggregation for analysis. The data from responses were downloaded into CSV Excel format for initial frequency distribution visualizations analysis. Subsequently, the data were imported into the Statistical Package for Social Sciences (SPSS) to conduct descriptive statistical analyses. The means were ranked to identify the relative adoption levels of various big data tools and their drivers in airport.

Results

Demographic Characteristics

The demographic distribution of participants in Table 1 showed that 40.1 % participants were from airport A. Airport C had 31.7 % participants, while Airport B had 28.2 %. The distribution showed a balanced representation across the three selected airports. This balance could enhance the generalizability of the research findings across different airport. The educational attainment showed that 85.7 % of participants were graduates, 10.7 % had postgraduates’ degrees, while only 3.6 % senior school certificates holders. The higher educational attainment among participants suggests that the study involved a well-educated population, which could positively influence responses and research outcome. It implied the participants had adequate education to answer questions on application of big data and big data tools in infectious diseases prevention. The result of the experience level revealed a concentration in the early to mid-experienced ranges with 25 % of participants having less than 3 years of experience, 27.4% in the 3-5 years’ experience range and 21.4 % in the 6-8 years bracket. The smaller percentages in the older brackets (over 9 years). The result indicates a workforce with relatively young. This experience could affect the perception of the participants as it reflects the views of relatively early to mid-career individuals.

Table 1: Demographic Characteristics of the Research Participants

Big Data Tools and Technologies, Adoption Area, Drivers, Effectiveness and Challenges

Table 2 revealed big data tools and technologies most used at selected airports with mean scores ranging from 3.24 to 3.99. This higher mean score indicated adequate and acceptance of big data tools in the listed areas. The computer-based PCR machine had the highest mean of 3.99, which suggested it is the most widely used data collection tool among the airports. This higher mean score, coupled with a relatively low standard deviation (0.972), pointed toward consistent and prevalent use across the airports. In similar vein, hardware connectable sensors and internet-enabled devices followed with mean scores of 3.79 and 3.76 respectively. These tools demonstrated broad acceptance and integration in airport operations for infectious diseases detection. The higher acceptance and integration of these tools was likely due to their connectivity and real-time data acquisition capabilities. The moderate standard deviations (around 1.0 to 1.16) suggests a fair level of variation in use. It possibly reflects differences in infrastructure across the airports. Other sensor technologies such as chemical/gas sensors, sensor-based heat detectors and computer-aided big data tools maintain scores between 3.56 and 3.63. Although slightly lower than the top three, these means still indicated good application levels. The lowest mean scores appear for sensor-based smart glasses and headgear technologies (around 3.24–3.25), but these remain above the adequacy threshold of 3.0. This result showed the listed big data tools had acceptable integration across the studied airports.

                     Table 2: Types of Sensor-based Big Data Tools/Technologies Used at the Airports in Nigeria 

Table 3 showed the adoption and use of big data in diseases prevention at airports. The result revealed that 59.5 % of the participants reported that the airport has adopted big data, while 27.4 % have not adopted it and 13.1 % are unsure. Regarding the extent of adoption, 7.1 % of the respondents rated very low, 20.2 % as low, 35.7 % moderate, 26.2 % high and 10.7 % very high. This indicated that high-level of adoption rate, with over half of the respondents reporting that airports actively used big data tools for diseases detection. Additionally, the majority perceive adoption to be at a moderate to high level, which suggested that airport authority recognize the importance of leveraging big data in disease control at airports. It highlighted a trend towards using data-driven strategies to mitigate the health risks of infectious diseases.

                   Table 3: Adoption and Use of Big Data in the Prevention of Diseases’ Spread at the Airport

Table 4 presented the result of adoption and use of big data technologies in different areas related to risk of diseases outbreak mitigation within airport operations in Nigeria. The mean adoption report ranges from 3.64 to 4.43 with most adoption areas falling between 4.0 and 4.5-points. This suggests a generally positive attitude and acceptance of these technologies in enhancing airport operations to mitigation infectious diseases and safety measures. The result revealed that travel surveillance leads with the highest mean of 4.43, which indicates this area of application benefits most from big data tools. This high mean and relatively low standard deviation (0.713) suggested consistent use and its role in monitoring passenger flow to ensure security and health safety. Hazard-related applications, including hazard reduction, incident prevention, and employee/traveler’s protection, each have a mean of 4.24. These areas are evidently priorities for airport management, using big data to proactively mitigate risks and enhance safety. The consistency in means and standard deviations near 0.8 to 0.9 reflects strong but somewhat variable engagement across airports, likely influenced by operational complexity and resource availability. Other crucial uses such as hazard communication, awareness/detection, incident monitoring, investigation and hazard mapping also received acceptance rates between 4.02 and 4.18. Even the lowest mean score, 3.64 for “Other concerns,” remains well above the adequacy cutoff. This confirms a broad and positively perceived integration of big data technologies across multiple safety, surveillance and operational domains at airports. It showed the importance in enhancing infectious diseases risk management for passenger protection at the airports.

                               Table 4: Area of Adoption and Use of Big Data Technologies Airports

Table 5 showed the drivers influencing the adoption and utilization of big data tools and related technologies within airport operations. The result revealed that global pandemics and worker health and well-being emerge as the top drivers of big data adoption and use in the airport with mean score 4.64-points and 4.49-points. This result indicates that respondents strongly supporting initiatives that leverage Big Data to enhance infectious diseases prevention and employee welfare. The results highlighted industry's commitment to ensuring travelers’ health and well-being of its workforce as top drivers of big data and big data tools adoption at the airports.

Additionally, enhanced safety and risk mitigation stand out prominently as another key driver with an overwhelmingly high level of agreement among respondents as indicated by mean score of 4.46-points. In a sector where safety is paramount, the ability to harness big data for risk assessment and mitigation is important for maintaining operational integrity. Similarly, regulatory compliance to safety standards scored 4.04-points. This score showed that it is a significant driver of big data adoption and utilization. The aviation industry operates within a highly regulated framework, which necessitates the need for adherence to stringent safety protocols and standards. The data-driven insights also emerge as a primary driver with a significant majority of respondents expressing agreement towards leveraging big data for informed decision-making as indicated by the mean score of 3.83. The emphasis on data-driven insights aligns with the broader trend across industries where data analytics play a pivotal role in strategy formulation.

Efficiency/productivity, need for swift emergency response and technological advancements are additional drivers that enjoy substantial agreement among respondents. These drivers collectively reflect the industry's recognition of the transformative potential of big data in optimizing operations, enhancing responsiveness and staying abreast of technological innovations. Moreover, risk reduction for employees, competitive advantage, changing work environments and public perception further showed the multifaceted impact of big data adoption within airport operations. The results suggest a widespread acknowledgment within the aviation industry of the value big data tools offers and its potential to drive operational safety, diseases prevention and enhance stakeholder satisfaction.

                                 Table 5: Drivers of Adoption and Use of Big Data/Big Data Tools and Technologies

Table 6 illustrates the level of protection offered by big data and big data tools. The majority (71.4 %) either agreed or highly agreed that big data provides superior protection compared to traditional methods. This result showed a positive outlook on big data efficacy in infectious diseases prevention at the airports. However, a significant proportion (26.2 %) remained neutral implying some uncertainty or lack of clarity in understanding the extent of big data's benefits. When asked to estimated percentage protection provided by big data over traditional methods, more than half (56.0 %) estimated an improvement of 50 % or more, which indicated substantial confidence in the capabilities of big data. These results reflect optimistic stance toward big data's protective capabilities, with a notable subset expressing uncertainty. Further results highlighted diversity in perspectives among the sampled group, with some respondents attributing significant advancements to big data while others remain more cautious in their estimations. The results suggest a growing recognition of big data's potential to enhance preventive measures. Further research may be necessary to address concerns and uncertainties around the effectiveness of big data tools in infectious diseases prevention and control.

                                     Table 6: Comparison of the Level of Protection Provided by Big Data

The results in Table 7 shows concerns on adoption and use of big data and big data tools in the airport. Result revealed 73.8 % of participants expressed concern, 19.0 % reported no concern, while 7.1 % were either concerned or unconcerned. This indicates a significant level of apprehension among respondents about the implications of big data usage in the airport. Regarding safety concerns in airport operations, 73.8 % reported very high-level of concern, 19.0 % reported a high level and 7.1 % reported moderate extent of concern. This suggests that safety is a paramount issue for the majority of respondents, with a substantial portion expressing a very high level of concern. When asked to rate the level of safety and mitigation derived from the use of big data, 34.5 % rated very high, 52.4 % rated high, 11.9 % rated moderate and only 1.2 % rated it as very low. This result suggests maximal confidence in the level mitigation and safety big data provided. It showed that a significant portion of respondents believe in the potential of big data to enhance risk mitigation and safety. Despite some skepticism, a considerable portion of respondents opined that big data could provide high levels of safety and believed in the potential benefits of big data in enhancing diseases prevention at the airports.

                                            Table 7: Concern on Use of Big Data and Extent of Protection

Table 8 shows concerns big data adoption in airports. The result revealed that limited awareness level receiving the highest mean of 3.74, closely followed by security and privacy (3.73) and data ethics (3.71). These were the most significant concerns among the participants. Other concerns were data protection (3.68) and exposure to productivity pressure (3.67). Participants were equally concern on potentially employee surveillance (3.52), limited evidence of safety (3.43) and other concerns (3.33). The mean scores indicate that respondents were concerned on the current state of big data adoption and its application in airport.

                                              Table 8: Concerns on Big Data Adoption/Application in Airports

The result in Table 9 shows the challenges relate to big data adoption and use in disease prevention in the airports. Cost of obtaining big data tools ranked top with mean of 4.30, which revealed strong agreement on its impact on adoption and use. Other significant challenges such as the limited supply of big data tools (mean 4.01), lack of technical know-how (3.92) and maintenance and repairs (3.90) showed high recognition with emphasis on resource availability and technical capacity as major barriers to effective big data use. Other challenges include lack of regulatory standards (3.76), integration with existing systems (3.7) and resistance to change (3.69). This indicates there are organizational and systemic factors that influence the adoption and use of big data at the airport. Challenges with lower but still significant level of impact were privacy concerns (3.48) and cyber security threats (3.31). This highlights security and data protection as ongoing challenges in the adoption and use of big data and big data tools in the airport. The mean scores suggest that while various technical, financial and organizational hurdles exist, stakeholders generally acknowledge these challenges as valid and important for improving big data implementation in airport disease prevention efforts.

                                Table 9: Challenges of Big Data Adoption and Use in Airports for Diseases Preventions

Discussion of the Findings

The study’s participant demographic revealed a well-distributed representation from three Nigerian airports, with a majority being graduates and early to mid-career professionals. This implies a relatively informed and adaptive workforce, which is critical for the acceptance and effective implementation of big data technologies in infectious disease prevention. A study by Achieng and Ogundaini supports the notion that educated health workers enhance digital health adoption and implementation in sub-Saharan Africa [33]. While Achieng and Ogundaini collaborate the findings of this study on the role of educated workforce in digital adoption, Herron and Wolfe maintained that the younger or less experienced workforce observed in this study might lack the practical skills to leverage fully new technologies [34]. This lack of practical skills the duo believed could hinder the effectiveness of adopted technologies. The position of Herron and Wolfe agreed with the broader finding of this study on the challenges of big data adoption and utilization in infectious diseases prevention at the airport level in Nigeria. Additionally, the dominant use of computer-based PCR machines and sensor technologies shows robust integration of real-time, sensor-based data collection vital for early infectious disease detection at airports [34]. This aligns with global findings of Owuor, Sahraoui et al. and Xie et al. where sensor and PCR tech form the backbone of airport disease surveillance [35-37]. Xie et al. had maintained that emerging molecular diagnostic strategies are transitioning from traditional amplification methods like PCR to amplification-free biosensing approaches, which hold promise for rapid, safe and economically accessible respiratory disease diagnosis [37]. Xie et al. posited that the approach is suited for decentralized and home-based testing, believing that the technologies could optimized through integration with AI for enhanced infectious disease prevention and control [37]. Contrary to this position, Azzolini et al. argued that over-reliance on technology can result in blind spots if human factors and data interpretation skills are weak [38]. The finding was supported by recent reviews on amplification-free biosensors and AI integration in molecular diagnostics for respiratory diseases, which emphasize the potential for faster, safer and more accessible testing during outbreaks.

Moving further, over half of respondents confirmed adoption of big data tools for the prevention of infectious diseases at airports at moderate to high-level. The finding indicates a rising reliance on data-driven approaches for infectious disease control. This is consistent with global trends of increasing big data integration in public health surveillance [39,40]. However, research from some African contexts, for instance, Aborode et al. highlighted persistent gaps and uneven adoption rates due to infrastructure and policy constraints [41]. The strong adoption of big data in travel surveillance and hazard functions underscores its critical role in operational safety and infectious disease risk mitigation. Gilbert et al. reported that predictive and real-time monitoring of infectious diseases was well-documented in outbreaks prevention linked to travel hubs [42]. Yet, others like Matini et al., and Owolabi and Owolabi cautioned that without integrated data systems, these benefits of predictive and real-time monitoring of infectious diseases might not translate into effective on-the-ground interventions [43,44]. While this position maybe true, predictive and real-time monitoring of infectious diseases with big data tools and technologies holds enormous benefits in future infectious diseases control and epidemiological responses. The study identified pandemics, worker well-being and safety as primary drivers of big data uptake. The finding reinforced the need for health and operational safety as well as how infectious diseases prevention and control are becoming public health priorities. This driver profile matches findings from Jee on International Health Regulations and Fonka et al., on drivers of pandemic response frameworks [45,46]. Contrarily, some critiques like Beacher et al. and Kalaiarasan suggested that economic and political factors often overshadow health-centric motivations in technology adoption decisions [47,48]. While the argument of the critiques is true, considering the case of Anthony Fauci and Donald Trump during the COVID-19 outbreaks, fact remained that pandemics, worker well-being and safety will continue to be the primary drivers of big data technology adoptions [49,50]. Additionally, the fact that most participants believed big data offers superior protection over traditional methods do not only reflect optimism in its enhanced surveillance capacity, but also its potential position in future health emergencies. Idahor et al. echoed similar optimism in the use of big data and AI in the COVID-19 pandemic for outbreak prediction and resource allocation [51]. Nonetheless, Summers et al. pointed out skepticism in the use of big data over data accuracy and privacy issues limiting full confidence (ECDC report on digital surveillance) [52]. While concerns about safety and big data use are high, the majority of the respondents acknowledge its contribution to risk mitigation. This dual perspective aligns with findings from Ghana and Tanzania where Mendonça and Mafra reported improved safety outcomes in infectious diseases control with big data, and the fueled ethical debates around surveillance and consent [53]. Aborode et al. shared opposing views stressing that unregulated big data use risks exacerbating inequalities and infringing rights, which may compromise overall benefit [41].

Ethical and privacy concerns, including limited awareness and employee surveillance, were prominent among the challenges of big data adoption and use. It highlights the need for careful handling and managing sensitive data responsibly. Studies by Achieng et al. confirm these are major barriers in SSA health systems, which necessitate transparent data policies, stakeholder education and cautious adherence to Helsinki declaration [33]. Contrastingly, Sun et al. and Egwuonwu et al. argued that these concerns are overstated citing technological safeguards as sufficient measures to address privacy if properly implemented. Consequently, challenges around cost, supply limitations, technical skills gaps and regulatory obstacles alongside cybersecurity threats can be bridged using appropriate measures [54,55]. These issues are consistent with findings across African airports and health systems, where resource constraints and weak governance impede full potential big data [41]. However, researches by KazançoÄ?lu et al. and Jha and Singh indicates that targeted investments and capacity-building programmes can rapidly overcome most of the barriers identified in this study and enable successful big data applications for infectious diseases prevention at airports level [56,57]. While these findings illustrate a complex but changing landscape in big data adoption in Nigerian airports for infectious disease prevention, it is characterizing by strong drivers and benefits tempered by significant technical, ethical and organizational challenges. The balance of studies affirms the transformative potential of big data while cautioning against unethical adoption without addressing contextual constraints and concerns.

Conclusion

Big data adoption for infectious disease prevention in Nigerian airports is decisively advancing with robust sensor-based PCR and emerging bio-sensing technologies. These tools enable rapid, real-time disease detection essential for effective outbreak control. While adoption is notably strong in travel surveillance and infectious hazard management, there is persistent significant challenges in data integration, practical skills gaps and ethical concerns such as privacy and employee surveillance. The primary motivations sustaining this progressive adoption are pandemics, worker well-being, and operational safety, which indicate the public health imperative driving these technological implementations. Despite optimism about big data’s capabilities to provide superior protections, skepticism around data privacy, accuracy and regulatory frameworks remains strong with potential to hinder adoption and use. Overcoming cost, technical capacity and governance related barriers through targeted investments and capacity-building initiatives could increase adoption and use. While this study confirms that big data technologies hold transformative potential for infectious disease control in airports, the full benefits can only be realized through ethically grounded strategies that address technical, organizational and social challenges.

Declaration

Ethical Approval

Ethical approval was received from Review Committee on Research Ethics of the Center for Disaster Risks Management and Development Studies of the Federal University of Technology, Minna, Niger State, Nigeria.

Inform Consent

Informed consent complied with Helsinki Declaration of June 1964 as reviewed in 2024. All participants voluntarily chose participates having understood fully the language, objectives, methods, potential benefits and risks of the study.

Data Availability Statement

The datasets generated and analyzed during the current study are not publicly available in compliance to Part VII of Data Security (Sections 39 to 40) of the Nigeria Data Protection Act (NDPA) 2023, which require data handlers to secure personal data, maintaining confidentiality and integrity, but are available from the corresponding author on reasonable request.

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