Qualitative Assessment of the Use of Big Data and Big Data Tools in the Prevention of Epidemiological Diseases’ Spread Through Airports

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A Kuti, S Ajobo, U Ndagi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7931768/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The global spread of epidemiological diseases because of inter-country travels through the airports raises concern on the safety and health of the global community. The 2019 coronavirus outbreak that started from Wuhan China and spread to other parts of the world is a typical example. Efforts toward preventing global epidemiological diseases’ spread through the international airport had led to the use of big data and big data technologies. This research investigate the role of big data in the management of epidemiological diseases in aviation industry. The study adopted qualitative research approach using checklist and interviews. The qualitative data were obtain through in-person and telephone interview. The data were analysed using thematic and content analysis. Result showed that Sensor-based Smart Cameras (CCTV), computer-based PCR machines, Fingerprint Biometric Identification and Electronic Health Records (EHR) Systems and Bluetooth Low Energy (BLE) Beacons were among the big data collection tools used at the airports. The performance of the airports in term of big data/big data tools availability, functionality and operations was 70%, which showed s high-level of compliance to international best practices by the airports. Top concerns were data privacy, high cost of big data tools and technologies, and limited supplies. The study concluded that there is high-level of utilization and compliance to standards in the ways big data were use among the airport, with consensus on the usefulness of these tools in diseases detection and prevention at airports. It recommends developing robust data governance policies that prioritize the protection of passengers and employees’ data while ensuring compliance with regulatory standards. This ensure that travelers safety and guarantees the mitigation of diseases’ spread through the airports. Qualitative assessment big data big data tools prevention epidemiological diseases disease spread airports Figures Figure 1 1 INTRODUCTION Epidemiological diseases, which often resulted in disasters, are major global concern because of their health, economic and sociopolitical impacts [ 1 ]. Historically, diseases epidemiology related disasters recorded the highest number of live losses when compared to other form of disasters and it is a common secondary consequence of other disasters such as earthquakes, flooding, droughts, wars, avalanches, tsunami and heat waves among other types [ 2 ]. Global spread of epidemiological diseases has been on the rise with increase in global travels enabled by the aviation industry. Typical examples are the H1N1 pandemic that started on 15th March, 2009. It was discovered on 18th March, 2009 and was declared a public health emergency of international concern (PHEIC) on 25th April, 2009. The Ebola epidemic in West Africa that started 26th December, 2013, was discovered on 22nd March, 2014 and designated a PHEIC on 8th August, 2014. Then Zika virus infections, which started on 22nd October, 2015 due to rise in microcephaly cases and was linked to the Zika virus with strong evidence. The Zika virus epidemic was designated a public health emergency of international concern (PHEIC) on 1st February, 2016 [ 3 ]. The global impact of epidemiological diseases has catalyzed the emergence of Precision Public Health (PPH) as a vital multidisciplinary field, as highlighted by Khoury et al . [ 4 ]. PPH refers to population-level activities rather than individualized medical interventions, a departure from the conventional use of the term in precision medicine. PPH leverages big data and data science to revolutionize public health practices. While precision medicine has been primarily associated with genomic advancements, it has raised concerns about on its limited focus on environmental and social determinants of population health. Researchers have elucidated the tension between population health and precision medicine with emphasis the necessity of integrating personalized and population-level interventions to enhance health outcomes. The proliferation of big data presents unprecedented opportunities for the widespread implementation of PPH approaches, complemented by methodological innovations to address potential limitations [ 5 ]. The concept of PPH was introduce formally in peer-reviewed literature in 2016, urging the modernization of surveillance systems and targeted interventions from a population health perspective [ 6 ]. Subsequently, in 2019, the scope of PPH was expanded beyond individual genomic variation, thereby advocating for the utilization of novel data sources and analytics to enhance public health surveillance and intervention strategies across temporal, spatial and demographic dimensions. PPH is recognize as both a global and national public health imperative with diverse approaches tailored to specific contexts [ 7 ]. The onset of the COVID-19 pandemic in early 2020 prompted a heightened awareness and action from global health authorities including the World Health Organization (WHO), which declared it a public health emergency of international concern [ 7 ]. In response to the pandemic, authorities in the Kingdom of Saudi Arabia implemented stringent measures to mitigate its spread domestically [ 8 ]. Adhering to WHO guidelines, nations including Saudi Arabia's Ministry of Health (MoH), implemented protocols for identifying and isolating suspected COVID-19 cases. Despite these efforts, the pandemic continued to proliferate rapidly, resulting in millions of infections and fatalities worldwide [ 9 ]. The dynamic nature of the pandemic characterized by evolving transmission patterns and varied clinical manifestations posed significant challenges to containment efforts and strained healthcare systems globally [ 10 ]. To facilitate early detection and tracking of COVID-19 cases, individuals with access to remote detection systems were subjected to regular monitoring. The utilization of such systems generates vast amounts of data, which presented opportunities for leveraging big data analytics tools to enhance healthcare quality. Software solutions from open-source Apache's big data designed for cloud computing environments facilitated the development of data-driven healthcare solutions [ 11 ]. The term "big data" encompasses data characteristics such as variability, velocity, volume, variety and veracity although the original definition focused on volume, velocity and variety [ 12 ]. These characteristics are particularly pertinent in the healthcare sector, where big data analytics tools hold immense potential for improving service delivery and performance. Applications of big data analytics in healthcare span a broader area such as surveillance, risk communication, disease detection, personalized healthcare, drug discovery, clinical research, genomics and various medical specialties such as gynecology, nephrology and oncology [ 11 , 13 ]. However, the field of big data in healthcare management is still evolving with rapid efforts to harness its full potential [ 14 ]. The increasing frequencies, spreads and global impact of epidemiological diseases outbreaks, particularly through international and national travels airports raises question on the role big data technologies can play in curbing the menace. This paper investigate the role of big data in the prevention of epidemiological diseases’ spread through in aviation travels in Nigeria. 2 METHODOLOGY The research employed qualitative research method to gather the requisite data to drive the objectives of the study. The choice of this research strategy was directly influenced the nature and level of data required. The approach helped to achieved qualitative and in-depth insight by combining qualitative observational and interview data. This approach expedites data collection from a specialised pool of participants within a short timeframe and leveraging on the method’s ability for simultaneous data collection. Ethical approval was received from the Ethical Review Committee of the Center for Disaster Risks Management and Development Studies of the Federal University of Technology, Minna, Niger State, Nigeria and Inform Consent was obtained from each of the airports, A, B and C where the research was conducted and the individual interviewees. The research focused on engaging participants across three distinct tiers of senior management within airport operations from airports A, B and C with three individuals interviewed in each airport. The criteria for the selection of airports was based on rankings in terms of total number of airlift per year. This study gathered primary qualitative data from stakeholders within Nigeria's aviation and air transport industry. Cross-sectional interviews were employ to investigate the current landscape of big data applications within the airports [ 15 ]. The interview employed structured open-ended questions to obtain qualitative primary data from key personnel of the airports. The interview enquiries were conducted through different means such as in-person, via virtual meetings on Zoom and Whatapps base on conveniences for both the interviewer and the interviewees. The interview take into account need for in-depth exploration consonant to global best practices. The interview transcripts with top airport managements were analysed according to the methods of Braun and Clarke [ 16 ]. The transcripts were carefully coded using the six-phase method to explicitly capture the underlying meanings. The NVIVO software was used to identify trends in the coded transcript as suggested by Kim et al . [ 17 ], while the patterns were organized into themes and sub-themes. The themes and sub-themes were reviewed thoroughly to ensure accurate placement and minimize naming errors as recommended by Braun and Clarke [ 18 ] and Byrne [ 19 ]. The outcomes of the analysis was presented in a table or on a thematic map. 3 RESULTS Table 1 showed the result of the checklist observation on available big data tools and technologies used at airports A, B, C in Nigeria. The result showed that Magnetic Resonance Imaging (MRI) and X-ray devices was available and functional in airport A, B but not in C. Hand-held Thermal Imaging Cameras was available and functional, except that the HD version was unavailable at all airports. Similarly, biometric identification systems like fingerprint scanners were functional at all airports, whereas facial recognition and iris scanners were universally unavailable in the three airports. Electronic Health Records (EHR) systems, sensor-based smart cameras and RFID tags were available and functional across all airports. There was a mix of availability and functionality of Bluetooth Low Energy (BLE) Beacons and Geographic Information Systems (GIS) hardware. GPS tracking devices, mobile applications, IoT sensors, social media platforms, contact tracing apps and data analytics tools were available at all airports. This result identify gaps in the deployment and functionality of big data tools available in Nigeria Airports for the prevention of the spread of epidemiological diseases through the airports. Table 4 Big Data Tools/technologies Used at the Airports SN Data Collection Tools Use Examples of Big Data Tools/Technology Available A B C 1 Magnetic Resonance Imaging (MRIs) devices Open and close-bore MRIs Machines AF AF ABNF 2 X-rays devices AF AF A F 3 CT scans 4 Thermal Imaging Cameras HD Outdoor Thermal Imaging (long range) U U U Hand Held Thermal Imaging (close range) AF AF AF 5 Biometric Identification Systems Fingerprint scanners AF AF AF Facial recognition and iris scanners U U U 6 Electronic Health Records (EHR) Systems Health data-base A A A 7 Sensor-based smart cameras CCTV Cameras AF AF AF 8 Bluetooth Low Energy (BLE) Beacons - U A U 9 RFID Tags - AF AF AF 10 Sensor-based heat detectors Thermal Imaging Cameras AF AF AF 11 GPS Tracking Devices GPS Tracking Devices A A A 12 Geographic Information Systems (GIS) Hardware A A A Software AF AF AF 13 Computer-based PCR machine PCR Machines AF AF AF 14 Mobile applications for big data generations BP Apps, GPS Coordinate Apps A A A 15 Internet of Things (IoT) Sensors - A A A 16 Internet of Things (IoT) and social media Twitter, Facebook, Instagram …. A A A 17 Contact Tracing Apps Corona-Warn-App, Radar COVID, TraceTogether, COVID Alert, COVIDSafe and others A A A 18 Data Analytics Tools and Technologies Data Analytics Platforms, ArcGIS, Google analytics, Power BI, Tableau, among others A A A Note: A = Available, AF = Available and functional, U = Unavailable, and ABNF = Available but non-functional Table 2 shows the Big Data tools and technologies used in Airports. The airports have embraced a plethora of big data tools and technologies to enhance operations that ensure passenger safety and streamline processes. Thermal Imaging Cameras play an important role in fever screening to enable detection of individuals with elevated body temperatures, which potentially indicates illness. The focus on medical and temperature data, airports can swiftly identify and isolate individuals who may pose a health risk to others. Sensor-based Smart Cameras (CCTV) provides surveillance and monitoring system that enhance security across various airport areas. They primarily collect image-based data for security purposes. Biometric Identification Systems were use for fingerprint, facial recognition and iris scanning. These biometric systems enhance security by accurately identifying and authenticating passengers and staff. This not only ensures a seamless travel experience but also enables the collection of passengers' medical history and health status for enhanced safety measures. In addition, Electronic Health Records (EHR) Systems were digitize to management patients' medical records through EHR systems, which facilitates easy access and retrieval of information that aid efficient healthcare delivery within airport premises. Bluetooth Low Energy (BLE) Beacons were used for real-time tracking of passenger and staff movement within the airport to collect data on passenger position for efficient monitoring of passenger contact, crowd management and health resource allocation. RFID Tags were used for seamless tracking of luggage and boarding passes, which optimize baggage handling processes and enhancing passenger experience. Geographic Information Systems (GIS) aids spatial data analysis that enable the airports to visualize and understand patterns related to diseases spread, passenger movements and infrastructure planning. Additionally, GPS Tracking Devices were attach to passenger wears or seat to tracking and monitor individual movements within the airport premises, particularly in the plane. This help to ensure efficient diseases prevention and management through contact tracing of individual movements. Computer-based PCR Machines facilitate passenger testing by amplifying DNA segments and accurately detect molecular characteristics of infectious virus, bacterial and fungi. This ensures that only passenger free from potential viral, bacterial or fungi pathogens were booked for travel. Mobile applications were used for health status reporting, symptom tracking and travel history submission. This provide valuable health data for disease surveillance and management. Internet of Things (IoT) sensors were deployed for monitoring environmental factors. These IoT sensors ensure optimal conditions within the airport premises and contribute to passenger comfort and well-being. Similarly, Internet of Things (IoT) and Social Media integrated platforms were use valuable channels for incident reporting, disease information dissemination and data sharing. The approach enhance risks communication and collaboration among airports, stakeholders and travelers. Contact Tracing Apps were used to aid tracking and notifying individuals who may be exposed to infectious diseases within the airport environment. It facilitates timely intervention and containment efforts. In similar vein, Data Analytics Tools and Technologies platforms like ArcGIS, Google Analytics and Power BI were employ to enable airports authority process large volumes of data for insights into disease transmission, passenger movements and operational efficiency. The adoption and integration of these big data tools and technologies at airports represents a proactive approach towards enhancing public safety, security and efficiency. Table 2 Big Data Tools/technologies Used at Airports SN Data Collection Tools Use Examples of Big Data Tools/Technology How it is being used Primary Type of data Collected a Thermal Imaging Cameras HD Outdoor Thermal Imaging (long range) and Hand Held Thermal Imaging (close range) These cameras are used for fever screening and detecting individuals with elevated body temperatures, which may indicate potential illness or infections. Medical and temperature data b Biometric Identification Systems Fingerprint scanners, Facial recognition and iris scanners Biometric systems such as fingerprint scanners, facial recognition and iris scanners are used for secure identification and authentication of passengers and staff. Passengers medical history and health status c Electronic Health Records (EHR) Systems Health data-base EHR systems are used to manage and store patients' medical records digitally, which allow for easy access and retrieval of travelers’ information. Health records (medical records) d Sensor-based smart cameras Closed-Circuit Television (CCTV) Cameras CCTV cameras are used for surveillance and monitoring purposes in various areas of the airport, including terminals, baggage handling areas and parking lots. This aid contact tracing. Image-based data e Bluetooth Low Energy (BLE) Beacons - BLE beacons are place strategically throughout the airport to track the movement of passengers and staff in real-time. - f RFID Tags - RFID tags are attached to luggage or boarding passes to track their movement and ensure efficient baggage handling. Passengers/passenger luggage data g Sensor-based heat detectors Thermal Imaging Cameras These cameras are used for fever screening and detecting individuals with elevated body temperatures, which can be an indication of potential illness. Medical data h GPS Tracking Devices GPS Tracking Devices GPS tracking devices attached to luggage or worn by individuals to track their movements within the plane or airport premises. Position or location data i Geographic Information Systems (GIS ArcGIS GIS technology are used to analyze and visualize spatial data related to disease outbreaks, passenger movements and airport infrastructure. Location data j Computer-based PCR machine PCR Machines PCR machines are used for passenger testing through cyclic amplification of DNA segments at specific temperature changes. It enables detection of viral or pathogens DNA in infected travelers. Viral, bacterial and fungal organisms’ detection and Medical data k Mobile Applications - Passengers to report their health status, symptoms, and travel history before or during their journey can use mobile apps. Infectious diseases risk data l Internet of Things (IoT) Sensors IoT sensors deployed to monitor environmental factors such as air quality, humidity and temperature in the airport premises. Air quality and temperature data m Internet of Things (IoT) and social media Twitter, Facebook, Instagram …. Social media handles of airports are used for incident reporting, diseases information dissemination and data sharing Incident data n Contact Tracing Apps Corona-Warn-App, Radar COVID, TraceTogether, COVID Alert, COVIDSafe and others Contact tracing apps used to track and notify individuals who may have come into contact with an infected person within the airport environment. Location as well as contact data o Data Analytics Tools and Technologies Data Analytics Platforms, ArcGIS, Google analytics, Power BI, Tableau, among others Data analytics platforms can process large volumes of data collected from various sources to identify patterns, trends, and anomalies related to disease transmission and surveillance. Medical and location data in sights Figure 1 shows the thematic map of the interview transcript on application of big data and big data tools/technologies used in airports. The result indicates that there are five central themes in the application of big data and big data tools/technologies to prevention of the spread of epidemiological diseases through airport. The five themes identified in the transcript were the type of big data tools/technologies and the data generated, area of application of big data and big data tools/technologies, drivers of adoption of the application of big data and big data tools/technologies, challenges of big data applications and ethical concerns. Each of the five themes were sub-themes further to provide specific information on the application of big data and big data tools/technologies to diseases’ spread prevention. The data type generated were medical, location, travelers’ identity, biometrics, contacts, medical characteristics and geographical data of passengers. In addition, pathogens molecular data generated using PCR machine big data tools/technologies. These data were used primary for different purposes, ranging from hazard identification and reporting, diseases surveillance, diagnosis and prevention as well as incident investigation and contacts tracing. In the area of drivers of adoption of big data and big data technologies to the prevention of epidemiological diseases’ spread as identified in the transcript were global pandemics, compliance and need for diseases outbreak risk reductions. Common challenges were privacy concerns, high cost of big data tools and technologies and limited supplies while data privacy was top in the ethical concerns. Table 3 shows benchmarking of big data/big data tools/technologies in Nigeria airports with global standards. The results indicate that there is very high-level of big data/big data tools/technologies availability at the three assessed airport. The airports scored 75–85% base on available big data and big data systems when compared to global standard. This implies there is very high-level of big data/big data tools/technologies availability at the three airports. In area of functionality, the airports scored 70–75% base on the functionality of the available big data and big data systems at the airports when compared with global standard. This result implied that the big data systems within the airports are highly functional base on the result in Tables 1 and 2 benchmarked against global standards. Also, there is adequate level of application of big data/big data tools and technologies as indicated by a score of 65–70%, which mean that the big data/big data tools and technologies are not just available and functional, the tools and their data products are adequately applied to the prevention of epidemiological diseases—outbreaks and spreads. Results equally showed that there is adequate availability of personnel to management the big data system within the assessed airport as shown by 60–70% availability of personnel. This result was supported by 55–70% operational responsiveness of management to epidemiological diseases outbreaks and curtailment score. This implies sufficient personnel availability and responsiveness to epidemiological diseases outbreaks and curtailment of spreads. Epidemiological diseases outbreak handling scored from adequate to high-level with 60–75% score when compared to global best practices. The result equally implied high-level of preparedness to handle the spreads of epidemiological diseases through the airports. Finally, in the area of operational efficiency, there three assessed airports score between 65–75%, which indicate high-level of operational efficiency in terms of personnel operation and availability of big data tools and technologies. The overall performance of the airports in term of availability, functionality and operations of big data/big data tools was 65–75%. Hence, the airports operation high-level of compliance to global best practices. Table 3 Compliance of the Airports with Global Standard on Big Data Technologies Used SN Area of comparison Nigeria Airports Score Base on Global Standard Means Scope Level of Compliance with Global Standard 1 Availability of big data tools and technologies Readily available 75–85% 80.0 Very high level of compliance 2 Functionality of the available big data tools and technologies Mostly functional 70–75% 72.5 High level of compliance 3 Level of application of big data/big data tools and technologies High level of application 65–70% 67.5 Adequate 4 Availability of personnel to manage the available big data/big data systems Available 60–70% 65.0 Adequately manned 5 Operational responsiveness of management to epidemiological outbreaks and curtailment Adequately responsive 65–70% 67.5 Average compared to global standard 6 Handling of epidemiological outbreak Highly adequate 60–75% 67.5 Highly competent 7 Operational efficiency of the big data/big data tools and technologies Adequate to high 65–75% 70.0 Highly operational 8 Overall performance of airports in big data/big data tools availability, functionality and operations Highly adequate 65–75% 70.0 High level of compliance compare to global best practices Note: Performance below 50% are considered poor, 50–69% were considered adequate, 70–79% highly adequate and 80–100%. 4 DISCUSSION OF THE FINDINGS The study found that MRI and X-ray devices are available and functional at Airports A and B but unavailable or non-functional at Airport C. This finding reflects potential differences in funding, infrastructure or logistical challenges with Airport C possibly having limited resources or facing maintenance issues. Smith et al . [ 20 ] and World Health Organization [ 21 ] highlighted that resource disparities in developing regions often affect medical technology deployment for diseases treatment and prevention. Similar findings showed that MRI and X-ray availability correlate with airport size and funding. On the contrary, Huang et al . [ 22 ] argued that even smaller airports should prioritize these critical devices for epidemic control, as their absence can weaken disease detection and surveillance efforts. This finding showed there is a clear need for strategic investment in Airport C to ensure standardized diagnostic capability across airports for effective disease control. Furthermore, hand-held thermal imaging cameras are functional at all airports, but HD thermal cameras are completely absent. The reason could be associated with cost as hand-held camera devices are typically more affordable, portable and easier to operate compared to HD thermal imaging cameras. The cheap and easy to operate nature of hand-held cameras explained their widespread use in the airports. However, their HD versions may be cost-prohibitive or the airports may lack technical support for mounting such tool for health data collection. Keahey [ 23 ] demonstrated that hand-held thermal cameras offer effective fever screening in resource-limited settings, despite lower precision compared to HD models. However, Manullang et al . [ 24 ] caution that absence of HD thermal cameras may compromise early detection due to less accurate temperature readings. While hand-held cameras meet baseline-screening needs, there is however needs to upgrade to HD devices to improve diagnostic accuracy and disease prevention potential. On fingerprint scanners, this study found that all airports have functional fingerprint scanners, whereas facial recognition and iris scanners are not available. This availability of fingerprint scanners are cheaper, more established and easier to integrate than advanced biometrics, which demand high-end infrastructure and raise privacy concerns. Ode-Martins [ 25 ] report fingerprint systems as preferred biometric tools in many airports due to its cost-benefit advantages. Contrarily, Huang et al . [ 22 ] argued that lack of facial recognition limits security and disease control and response potentials of the airports, especially as it enables touchless identification. Expanding biometric technologies should balance cost, privacy and operational efficiency for enhanced security and diseases prevention. Findings from this study showed EHR systems, sensor-based smart cameras (CCTV) and RFID tags were available and functional across all airports. The availability of these systems are foundational for healthcare data management, surveillance and diseases/assert tracking. Hence, prioritized deployment of technology during health emergency can help to further strengthen health surveillance and spread tracking. This position aligns with the Study of Mohamed and Al-Azab [ 26 ], who emphasize the roles of these tools in streamlining airport health services and logistics. Contrary to Mohamed and Al-Azab, Ștefan et al . [ 27 ] posited that technologies alone are insufficient without integration of comprehensive health protocols into such technological deployment. This shows that consistent use of EHR and RFID indicates effective adoption that support operational integrity of the airports in diseases surveillance. Additionally, findings from this study revealed a mixed availability and functionality of Bluetooth Low Energy (BLE) Beacons and GIS hardware. The reason for this variability in deployment may be due to differing technical capacity or budget limitations among airports. Ibrahim et al . [ 28 ] link variable in beacon performance to infrastructure disparities. This infrastructure disparities maybe technical capacity, level of personnel’s technological-know-how or budget limitations. While disparities existed, Vo et al . [ 29 ] call for more uniform adoption of BLE to maximize health data tracking and spatial analysis efficiency. Standardizing BLE and GIS deployment would improve spatial data utility and contact monitoring for effective diseases prevention and control within and outside the airports. Findings indicated that GPS, Mobile Applications, IoT Sensors, Social Media, Contact Tracing and Data Analytics Tools were universally available across the studied airports. These tools are relatively cost-effective, scalable and supported by global standards in encouraging digital health surveillance. Blišťanová et al . [ 30 ] highlighted that their role in pandemic responses within the airports are worldwide. This implies that preventing diseases spreads at airport-level required both availability and functionality of these tools and software technologies for data gathering and scalability. On the contrary, Gisond et al . [ 31 ] and Xiao et al . [ 32 ] warned about data privacy and potential misinformation risks from social media reliance. The warning remained a valid point considering the negative impact of misinformation on health emergency responses. The widespread availability of the tools and technologies signals strong readiness for big data adoption in diseases responses, which should be match with ongoing management of data privacy and accuracy applications. Additionally, findings revealed that big data tools perform targeted roles that ranges from fever screening (thermal cameras), authentication (biometrics), record management (EHR), surveillance (CCTV), real-time tracking (BLE beacons, GPS), pathogen detection (PCR), health reporting (mobile apps), environmental monitoring (IoT), communication (social media), exposure notification (contact tracing apps) to data analysis (analytics tools). The reason is that each tool is uniquely design, adapted and employed to addresses specific needs for disease prevention, passenger safety and airport operation efficiency. Studies by Chowdhury [ 33 ], Alam et al . [ 34 ] and Ahmed et al . [ 35 ] all provided evidence of such multifaceted applications of big data and big data technologies for collection of real-time healthcare data tracking. Contrarily, Troxell and Sprague [ 36 ] highlighted the risks of over-dependence on technology and the need for human oversight, as technological tools are prone to errors and cyber threats. Giudice da Silva Cezar and Maçada [ 37 ] and Hussain et al . [ 38 ] on the other hand, stress digital literacy challenge that limits the application of big data technology, which may result in technological inaccuracies and errors in data collection and applications of such data. Hence, a comprehensive use of big data tools, even though it demonstrated effective, might require integration of human efforts with a balanced human-technology collaboration. This remains an important facet in the adoption and integration of big data into diseases prevention and epidemiological responses within and outside the airport settings. Also, the study’s findings indicated that d ata generated from the use of big data tools include medical, location, biometrics, contact, pathogen molecular and geographic data used in hazard identification, surveillance, diagnosis, prevention, investigation and contact tracing. Additionally, the adoption of big data technologies were driven by pandemics, compliance and disease risk reduction, while common challenges were privacy concerns, costs and supply limits. Privacy emerged as the most dominant ethical issue experienced in the use of big data tools and technologies. The reason is that pandemics have accelerated demand for data-driven interventions, and there is an equal need for intensifying concerns around privacy and resource allocation. Studies by Javed et al . [ 39 ], Olaboye et al . [ 40 ] and Igwama et al . [ 41 ] all identified these drivers and challenges, explaining that without adoption of big data tools designing interventions for health emergency responses and diseases control may become even more difficult, which may further pose greater threats to public health and healthcare systems. However, Buckman et al . [ 42 ] shared a contrary view on data privacy concerns stating that while privacies are important, implementing stringent data privacy rule may reduce the efficiency of public health responses. The study called for a relaxed data privacy rules in health emergencies. Bag et al . [ 43 ], on the other hand, suggested that decreasing costs of big data tools might alleviate supply issues. This implies that effective data policies would involve balancing data utility and privacy coupled with cost management. These are vital for sustained big data adoption and use in the prevention of diseases outbreaks and their spreads through international travels. Findings from this research revealed that Nigerian airports had high rating in big data and big data generating tools availability, functionality, application, personnel sufficiency, competent outbreak handling and operational efficiency when compared to global standards. They demonstrated strong readiness and capacity in managing epidemiological risks using advanced big data technologies. This high rating could be link to institutional commitment and pandemic urgency, which have accelerated technological adoption and capacity building. Amoo [ 44 ] and Ogunboye [ 45 ] reported similar progress in other emerging economies. However, Olayinka [ 46 ] argued that quantitative benchmarking may masked the gaps in real-time responsiveness. In summary, however, Nigerian airports are position technologically for diseases prevention and health emergency responses. Hence, strengthening the real-time management and continuous personnel training will optimize outcomes and reduce the spread of diseases through inter-state and international air travels. 5 CONCLUSION The study concludes that disparities in medical technology across airports affect their capacity for effective disease prevention as some airports lack important diagnostic tools. While handheld thermal cameras and fingerprint scanners are widely used due to affordability, advanced technological tools like HD thermal cameras and biometric systems remain limited by cost and privacy concerns. Conversely, digital tools such as Electronic Health Records and GPS are consistently functional, which support robust disease surveillance, risk detection and emergency response. Although uneven adoption of technologies like BLE beacons highlighted gaps in readiness, it could be concluded that the assessed airports have baseline big data tools for the detection and prevention of diseases spreads through air travels. Big data tools play vital and multifaceted roles in managing epidemiological risks, but challenges such as data privacy, technological literacy and resource constraints must be addressed to improve application of available. Also, Nigerian airports showed strong capability and commitment in applying big data technologies to prevention and reduced transmission of epidemic via air travels. It is suggested that continued investment in technology, training and real-time management will further strengthen disease control efforts and reduce epidemic risks from air travels. Declarations Funding: No funding was received for this research, as the research was self-funded. Conflict of Interest: There is no conflict of interest. Dual Publication: No dual publication submission. Clinical Trial Number: Not applicable Author Contribution Ajodo E. U. collected the data and compiled the research paper. Kuti I. A. supervised the research and contributed to improving the structure and content. Ajobo S. assisted in the data collection and analysis. Ndagi U. co-designed the research and facilitated data collection. All authors discussed the results and contributed to the final manuscript. References World Health Organization. (WHO, 2015). Health in 2015: from MDGs, millennium development goals to SDGs, sustainable development goals. World health organization. Retrieved from https://Wisnerepidemiology&otsonepage&q, 25th June 2023. Alexander D. (2018). Natural Disasters . Routledge. Retrieved from https://www.taylorfrancis.com/books/mono/10.4324/9781315859149/natural-disasters-david-alexander , 23rd June, 2022. Hoffman SJ, Silverberg SL. Delays in global disease outbreak responses: lessons from H1N1, Ebola, and Zika. 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IEEE Access. 2023;11:112891–928. Troxell G, Sprague C. (2024, July). The Human-Technology Spectrum: A Framework for Evaluating Sociotechnical System Function Allocation, Risk, and Performance. In INCOSE International Symposium (Vol. 34, No. 1, pp. 176–188). Giudice da Silva Cezar B, Maçada ACG. Data literacy and the cognitive challenges of a data-rich business environment: an analysis of perceived data overload, technostress and their relationship to individual performance. Aslib J Inform Manage. 2021;73(5):618–38. Hussain I, Sabir MR, ur Rehman N, Ghaffar I, Majeed KB. A spatial of Digital Technology, Digital Literacy, performance expectancy and techno stress in pandemic conditions in Technological institutes. J Disaster Recovery Bus Continuity. 2022;13(1):140–9. Javed I. Exploring the Role of Big Data in Predicting and Preventing Epidemics. Front Healthc Technol. 2024;1(1):46–57. Olaboye JA, Maha CC, Kolawole TO, Abdul S. Big data for epidemic preparedness in southeast Asia: An integrative study. Int Med Sci Res J. 2024;4(6):667–80. Igwama GT, Olaboye JA, Maha CC, Ajegbile MD, Abdul S. Big data analytics for epidemic forecasting: Policy Frameworks and technical approaches. Int J Appl Res Social Sci. 2024;6(7):1449–60. Buckman JR, Adjerid I, Tucker C. Privacy regulation and barriers to public health. Manage Sci. 2023;69(1):342–50. Bag S, Dhamija P, Luthra S, Huisingh D. How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. Int J Logistics Manage. 2023;34(4):1141–64. Amoo AB. (2024). Assessment of Constraints to the Implementation of International Health Regulation at the Point of Entry in Lagos Nigeria (Master's thesis, Kwara State University (Nigeria)). Ogunboye I, Adebayo IPS, Anioke SC, Egwuatu EC, Ajala CF, Awuah SB. (2023). Enhancing Nigeria’s health surveillance system: A data-driven approach to epidemic preparedness and response’. World J Adv Res Reviews, 20 (1). Olayinka OH. Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness. Int J Sci Res Arch. 2021;4(1):280–96. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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05:24:35","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132056,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7931768/v1/d1ced23f5dd70990990821f5.html"},{"id":94628112,"identity":"ba152317-0cdb-4007-a591-d09e4bc0ea8c","added_by":"auto","created_at":"2025-10-29 05:24:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThematic Map of Big Data/Big Data Technologies Used in the Airports\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7931768/v1/6a99ae9dd4d455463fe8b62e.png"},{"id":99311403,"identity":"9aca8b06-2b43-441e-8ee3-c45e0aabc16a","added_by":"auto","created_at":"2025-12-31 16:14:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":920949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7931768/v1/e24e463c-f12d-4abe-9bcb-afda30d1a4cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQualitative Assessment of the Use of Big Data and Big Data Tools in the Prevention of Epidemiological Diseases’ Spread Through Airports\u003c/p\u003e","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eEpidemiological diseases, which often resulted in disasters, are major global concern because of their health, economic and sociopolitical impacts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Historically, diseases epidemiology related disasters recorded the highest number of live losses when compared to other form of disasters and it is a common secondary consequence of other disasters such as earthquakes, flooding, droughts, wars, avalanches, tsunami and heat waves among other types [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Global spread of epidemiological diseases has been on the rise with increase in global travels enabled by the aviation industry. Typical examples are the H1N1 pandemic that started on 15th March, 2009. It was discovered on 18th March, 2009 and was declared a public health emergency of international concern (PHEIC) on 25th April, 2009. The Ebola epidemic in West Africa that started 26th December, 2013, was discovered on 22nd March, 2014 and designated a PHEIC on 8th August, 2014. Then Zika virus infections, which started on 22nd October, 2015 due to rise in microcephaly cases and was linked to the Zika virus with strong evidence. The Zika virus epidemic was designated a public health emergency of international concern (PHEIC) on 1st February, 2016 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe global impact of epidemiological diseases has catalyzed the emergence of Precision Public Health (PPH) as a vital multidisciplinary field, as highlighted by Khoury \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PPH refers to population-level activities rather than individualized medical interventions, a departure from the conventional use of the term in precision medicine. PPH leverages big data and data science to revolutionize public health practices. While precision medicine has been primarily associated with genomic advancements, it has raised concerns about on its limited focus on environmental and social determinants of population health. Researchers have elucidated the tension between population health and precision medicine with emphasis the necessity of integrating personalized and population-level interventions to enhance health outcomes. The proliferation of big data presents unprecedented opportunities for the widespread implementation of PPH approaches, complemented by methodological innovations to address potential limitations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe concept of PPH was introduce formally in peer-reviewed literature in 2016, urging the modernization of surveillance systems and targeted interventions from a population health perspective [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Subsequently, in 2019, the scope of PPH was expanded beyond individual genomic variation, thereby advocating for the utilization of novel data sources and analytics to enhance public health surveillance and intervention strategies across temporal, spatial and demographic dimensions. PPH is recognize as both a global and national public health imperative with diverse approaches tailored to specific contexts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The onset of the COVID-19 pandemic in early 2020 prompted a heightened awareness and action from global health authorities including the World Health Organization (WHO), which declared it a public health emergency of international concern [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn response to the pandemic, authorities in the Kingdom of Saudi Arabia implemented stringent measures to mitigate its spread domestically [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Adhering to WHO guidelines, nations including Saudi Arabia's Ministry of Health (MoH), implemented protocols for identifying and isolating suspected COVID-19 cases. Despite these efforts, the pandemic continued to proliferate rapidly, resulting in millions of infections and fatalities worldwide [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The dynamic nature of the pandemic characterized by evolving transmission patterns and varied clinical manifestations posed significant challenges to containment efforts and strained healthcare systems globally [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo facilitate early detection and tracking of COVID-19 cases, individuals with access to remote detection systems were subjected to regular monitoring. The utilization of such systems generates vast amounts of data, which presented opportunities for leveraging big data analytics tools to enhance healthcare quality. Software solutions from open-source Apache's big data designed for cloud computing environments facilitated the development of data-driven healthcare solutions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The term \"big data\" encompasses data characteristics such as variability, velocity, volume, variety and veracity although the original definition focused on volume, velocity and variety [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese characteristics are particularly pertinent in the healthcare sector, where big data analytics tools hold immense potential for improving service delivery and performance. Applications of big data analytics in healthcare span a broader area such as surveillance, risk communication, disease detection, personalized healthcare, drug discovery, clinical research, genomics and various medical specialties such as gynecology, nephrology and oncology [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the field of big data in healthcare management is still evolving with rapid efforts to harness its full potential [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The increasing frequencies, spreads and global impact of epidemiological diseases outbreaks, particularly through international and national travels airports raises question on the role big data technologies can play in curbing the menace. This paper investigate the role of big data in the prevention of epidemiological diseases\u0026rsquo; spread through in aviation travels in Nigeria.\u003c/p\u003e"},{"header":"2 METHODOLOGY","content":"\u003cp\u003eThe research employed qualitative research method to gather the requisite data to drive the objectives of the study. The choice of this research strategy was directly influenced the nature and level of data required. The approach helped to achieved qualitative and in-depth insight by combining qualitative observational and interview data. This approach expedites data collection from a specialised pool of participants within a short timeframe and leveraging on the method\u0026rsquo;s ability for simultaneous data collection.\u003c/p\u003e\u003cp\u003eEthical approval was received from the Ethical Review Committee of the Center for Disaster Risks Management and Development Studies of the Federal University of Technology, Minna, Niger State, Nigeria and Inform Consent was obtained from each of the airports, A, B and C where the research was conducted and the individual interviewees. The research focused on engaging participants across three distinct tiers of senior management within airport operations from airports A, B and C with three individuals interviewed in each airport. The criteria for the selection of airports was based on rankings in terms of total number of airlift per year.\u003c/p\u003e\u003cp\u003eThis study gathered primary qualitative data from stakeholders within Nigeria's aviation and air transport industry. Cross-sectional interviews were employ to investigate the current landscape of big data applications within the airports [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The interview employed structured open-ended questions to obtain qualitative primary data from key personnel of the airports. The interview enquiries were conducted through different means such as in-person, via virtual meetings on Zoom and Whatapps base on conveniences for both the interviewer and the interviewees. The interview take into account need for in-depth exploration consonant to global best practices.\u003c/p\u003e\u003cp\u003eThe interview transcripts with top airport managements were analysed according to the methods of Braun and Clarke [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The transcripts were carefully coded using the six-phase method to explicitly capture the underlying meanings. The NVIVO software was used to identify trends in the coded transcript as suggested by Kim \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], while the patterns were organized into themes and sub-themes. The themes and sub-themes were reviewed thoroughly to ensure accurate placement and minimize naming errors as recommended by Braun and Clarke [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and Byrne [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The outcomes of the analysis was presented in a table or on a thematic map.\u003c/p\u003e"},{"header":"3 RESULTS","content":"\u003cp\u003eTable\u0026nbsp;1 showed the result of the checklist observation on available big data tools and technologies used at airports A, B, C in Nigeria. The result showed that Magnetic Resonance Imaging (MRI) and X-ray devices was available and functional in airport A, B but not in C. Hand-held Thermal Imaging Cameras was available and functional, except that the HD version was unavailable at all airports. Similarly, biometric identification systems like fingerprint scanners were functional at all airports, whereas facial recognition and iris scanners were universally unavailable in the three airports. Electronic Health Records (EHR) systems, sensor-based smart cameras and RFID tags were available and functional across all airports. There was a mix of availability and functionality of Bluetooth Low Energy (BLE) Beacons and Geographic Information Systems (GIS) hardware. GPS tracking devices, mobile applications, IoT sensors, social media platforms, contact tracing apps and data analytics tools were available at all airports. This result identify gaps in the deployment and functionality of big data tools available in Nigeria Airports for the prevention of the spread of epidemiological diseases through the airports.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBig Data Tools/technologies Used at the Airports\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eData Collection Tools Use\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExamples of Big Data Tools/Technology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMagnetic Resonance Imaging (MRIs) devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOpen and close-bore MRIs Machines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eABNF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX-rays devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCT scans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eThermal Imaging Cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHD Outdoor Thermal Imaging (long range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHand Held Thermal Imaging (close range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBiometric Identification Systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFingerprint scanners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFacial recognition and iris scanners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElectronic Health Records (EHR) Systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealth data-base\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensor-based smart cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCTV Cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBluetooth Low Energy (BLE) Beacons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRFID Tags\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensor-based heat detectors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThermal Imaging Cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPS Tracking Devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGPS Tracking Devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGeographic Information Systems (GIS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHardware\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoftware\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComputer-based PCR machine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCR Machines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobile applications for big data generations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBP Apps, GPS Coordinate Apps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet of Things (IoT) Sensors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet of Things (IoT) and social media\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTwitter, Facebook, Instagram \u0026hellip;.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContact Tracing Apps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCorona-Warn-App, Radar COVID, TraceTogether, COVID Alert, COVIDSafe and others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData Analytics Tools and Technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData Analytics Platforms, ArcGIS, Google analytics, Power BI, Tableau, among others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cem\u003eA\u0026thinsp;=\u0026thinsp;Available, AF\u0026thinsp;=\u0026thinsp;Available and functional, U\u0026thinsp;=\u0026thinsp;Unavailable, and ABNF\u0026thinsp;=\u0026thinsp;Available but non-functional\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Big Data tools and technologies used in Airports. The airports have embraced a plethora of big data tools and technologies to enhance operations that ensure passenger safety and streamline processes. Thermal Imaging Cameras play an important role in fever screening to enable detection of individuals with elevated body temperatures, which potentially indicates illness. The focus on medical and temperature data, airports can swiftly identify and isolate individuals who may pose a health risk to others. Sensor-based Smart Cameras (CCTV) provides surveillance and monitoring system that enhance security across various airport areas. They primarily collect image-based data for security purposes.\u003c/p\u003e\u003cp\u003eBiometric Identification Systems were use for fingerprint, facial recognition and iris scanning. These biometric systems enhance security by accurately identifying and authenticating passengers and staff. This not only ensures a seamless travel experience but also enables the collection of passengers' medical history and health status for enhanced safety measures. In addition, Electronic Health Records (EHR) Systems were digitize to management patients' medical records through EHR systems, which facilitates easy access and retrieval of information that aid efficient healthcare delivery within airport premises. Bluetooth Low Energy (BLE) Beacons were used for real-time tracking of passenger and staff movement within the airport to collect data on passenger position for efficient monitoring of passenger contact, crowd management and health resource allocation. RFID Tags were used for seamless tracking of luggage and boarding passes, which optimize baggage handling processes and enhancing passenger experience.\u003c/p\u003e\u003cp\u003eGeographic Information Systems (GIS) aids spatial data analysis that enable the airports to visualize and understand patterns related to diseases spread, passenger movements and infrastructure planning. Additionally, GPS Tracking Devices were attach to passenger wears or seat to tracking and monitor individual movements within the airport premises, particularly in the plane. This help to ensure efficient diseases prevention and management through contact tracing of individual movements.\u003c/p\u003e\u003cp\u003eComputer-based PCR Machines facilitate passenger testing by amplifying DNA segments and accurately detect molecular characteristics of infectious virus, bacterial and fungi. This ensures that only passenger free from potential viral, bacterial or fungi pathogens were booked for travel. Mobile applications were used for health status reporting, symptom tracking and travel history submission. This provide valuable health data for disease surveillance and management. Internet of Things (IoT) sensors were deployed for monitoring environmental factors. These IoT sensors ensure optimal conditions within the airport premises and contribute to passenger comfort and well-being. Similarly, Internet of Things (IoT) and Social Media integrated platforms were use valuable channels for incident reporting, disease information dissemination and data sharing. The approach enhance risks communication and collaboration among airports, stakeholders and travelers.\u003c/p\u003e\u003cp\u003eContact Tracing Apps were used to aid tracking and notifying individuals who may be exposed to infectious diseases within the airport environment. It facilitates timely intervention and containment efforts. In similar vein, Data Analytics Tools and Technologies platforms like ArcGIS, Google Analytics and Power BI were employ to enable airports authority process large volumes of data for insights into disease transmission, passenger movements and operational efficiency. The adoption and integration of these big data tools and technologies at airports represents a proactive approach towards enhancing public safety, security and efficiency.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBig Data Tools/technologies Used at Airports\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData Collection Tools Use\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExamples of Big Data Tools/Technology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHow it is being used\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrimary Type of data Collected\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThermal Imaging Cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHD Outdoor Thermal Imaging (long range) and Hand Held Thermal Imaging (close range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThese cameras are used for fever screening and detecting individuals with elevated body temperatures, which may indicate potential illness or infections.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedical and temperature data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBiometric Identification Systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFingerprint scanners,\u003c/p\u003e\u003cp\u003eFacial recognition and iris scanners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiometric systems such as fingerprint scanners, facial recognition and iris scanners are used for secure identification and authentication of passengers and staff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePassengers medical history and health status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ec\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElectronic Health Records (EHR) Systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealth data-base\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEHR systems are used to manage and store patients' medical records digitally, which allow for easy access and retrieval of travelers\u0026rsquo; information.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth records (medical records)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensor-based smart cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClosed-Circuit Television (CCTV) Cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCTV cameras are used for surveillance and monitoring purposes in various areas of the airport, including terminals, baggage handling areas and parking lots. This aid contact tracing.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImage-based data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ee\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBluetooth Low Energy (BLE) Beacons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBLE beacons are place strategically throughout the airport to track the movement of passengers and staff in real-time.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRFID Tags\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRFID tags are attached to luggage or boarding passes to track their movement and ensure efficient baggage handling.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePassengers/passenger luggage data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensor-based heat detectors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThermal Imaging Cameras\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThese cameras are used for fever screening and detecting individuals with elevated body temperatures, which can be an indication of potential illness.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedical data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eh\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPS Tracking Devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGPS Tracking Devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGPS tracking devices attached to luggage or worn by individuals to track their movements within the plane or airport premises.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePosition or location data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ei\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeographic Information Systems (GIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArcGIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGIS technology are used to analyze and visualize spatial data related to disease outbreaks, passenger movements and airport infrastructure.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLocation data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ej\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComputer-based PCR machine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCR Machines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePCR machines are used for passenger testing through cyclic amplification of DNA segments at specific temperature changes. It enables detection of viral or pathogens DNA in infected travelers.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eViral, bacterial and fungal organisms\u0026rsquo; detection and\u003c/p\u003e\u003cp\u003eMedical data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ek\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobile Applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePassengers to report their health status, symptoms, and travel history before or during their journey can use mobile apps.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInfectious diseases risk data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003el\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet of Things (IoT) Sensors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIoT sensors deployed to monitor environmental factors such as air quality, humidity and temperature in the airport premises.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAir quality and temperature data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003em\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet of Things (IoT) and social media\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTwitter, Facebook, Instagram \u0026hellip;.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial media handles of airports are used for incident reporting, diseases information dissemination and data sharing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncident data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContact Tracing Apps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCorona-Warn-App, Radar COVID, TraceTogether, COVID Alert, COVIDSafe and others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContact tracing apps used to track and notify individuals who may have come into contact with an infected person within the airport environment.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLocation as well as contact data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData Analytics Tools and Technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData Analytics Platforms, ArcGIS, Google analytics, Power BI, Tableau, among others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData analytics platforms can process large volumes of data collected from various sources to identify patterns, trends, and anomalies related to disease transmission and surveillance.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedical and location data in sights\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the thematic map of the interview transcript on application of big data and big data tools/technologies used in airports. The result indicates that there are five central themes in the application of big data and big data tools/technologies to prevention of the spread of epidemiological diseases through airport. The five themes identified in the transcript were the type of big data tools/technologies and the data generated, area of application of big data and big data tools/technologies, drivers of adoption of the application of big data and big data tools/technologies, challenges of big data applications and ethical concerns. Each of the five themes were sub-themes further to provide specific information on the application of big data and big data tools/technologies to diseases\u0026rsquo; spread prevention.\u003c/p\u003e\u003cp\u003eThe data type generated were medical, location, travelers\u0026rsquo; identity, biometrics, contacts, medical characteristics and geographical data of passengers. In addition, pathogens molecular data generated using PCR machine big data tools/technologies. These data were used primary for different purposes, ranging from hazard identification and reporting, diseases surveillance, diagnosis and prevention as well as incident investigation and contacts tracing. In the area of drivers of adoption of big data and big data technologies to the prevention of epidemiological diseases\u0026rsquo; spread as identified in the transcript were global pandemics, compliance and need for diseases outbreak risk reductions. Common challenges were privacy concerns, high cost of big data tools and technologies and limited supplies while data privacy was top in the ethical concerns.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows benchmarking of big data/big data tools/technologies in Nigeria airports with global standards. The results indicate that there is very high-level of big data/big data tools/technologies availability at the three assessed airport. The airports scored 75\u0026ndash;85% base on available big data and big data systems when compared to global standard. This implies there is very high-level of big data/big data tools/technologies availability at the three airports. In area of functionality, the airports scored 70\u0026ndash;75% base on the functionality of the available big data and big data systems at the airports when compared with global standard. This result implied that the big data systems within the airports are highly functional base on the result in Tables\u0026nbsp;1 and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e benchmarked against global standards. Also, there is adequate level of application of big data/big data tools and technologies as indicated by a score of 65\u0026ndash;70%, which mean that the big data/big data tools and technologies are not just available and functional, the tools and their data products are adequately applied to the prevention of epidemiological diseases\u0026mdash;outbreaks and spreads.\u003c/p\u003e\u003cp\u003eResults equally showed that there is adequate availability of personnel to management the big data system within the assessed airport as shown by 60\u0026ndash;70% availability of personnel. This result was supported by 55\u0026ndash;70% operational responsiveness of management to epidemiological diseases outbreaks and curtailment score. This implies sufficient personnel availability and responsiveness to epidemiological diseases outbreaks and curtailment of spreads. Epidemiological diseases outbreak handling scored from adequate to high-level with 60\u0026ndash;75% score when compared to global best practices. The result equally implied high-level of preparedness to handle the spreads of epidemiological diseases through the airports. Finally, in the area of operational efficiency, there three assessed airports score between 65\u0026ndash;75%, which indicate high-level of operational efficiency in terms of personnel operation and availability of big data tools and technologies. The overall performance of the airports in term of availability, functionality and operations of big data/big data tools was 65\u0026ndash;75%. Hence, the airports operation high-level of compliance to global best practices.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCompliance of the Airports with Global Standard on Big Data Technologies Used\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea of comparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNigeria Airports\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eScore Base on Global Standard\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMeans Scope\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLevel of Compliance with Global Standard\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAvailability of big data tools and technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReadily available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75\u0026ndash;85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery high level of compliance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFunctionality of the available big data tools and technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMostly functional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u0026ndash;75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh level of compliance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel of application of big data/big data tools and technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh level of application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65\u0026ndash;70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdequate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAvailability of personnel to manage the available big data/big data systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60\u0026ndash;70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdequately manned\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOperational responsiveness of management to epidemiological outbreaks and curtailment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdequately responsive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65\u0026ndash;70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage compared to global standard\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHandling of epidemiological outbreak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHighly adequate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60\u0026ndash;75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHighly competent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOperational efficiency of the big data/big data tools and technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdequate to high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65\u0026ndash;75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHighly operational\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall performance of airports in big data/big data tools availability, functionality and operations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHighly adequate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65\u0026ndash;75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh level of compliance compare to global best practices\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cem\u003ePerformance below 50% are considered poor, 50\u0026ndash;69% were considered adequate, 70\u0026ndash;79% highly adequate and 80\u0026ndash;100%.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4 DISCUSSION OF THE FINDINGS","content":"\u003cp\u003eThe study found that MRI and X-ray devices are available and functional at Airports A and B but unavailable or non-functional at Airport C. This finding reflects potential differences in funding, infrastructure or logistical challenges with Airport C possibly having limited resources or facing maintenance issues. Smith \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and World Health Organization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] highlighted that resource disparities in developing regions often affect medical technology deployment for diseases treatment and prevention. Similar findings showed that MRI and X-ray availability correlate with airport size and funding. On the contrary, Huang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] argued that even smaller airports should prioritize these critical devices for epidemic control, as their absence can weaken disease detection and surveillance efforts. This finding showed there is a clear need for strategic investment in Airport C to ensure standardized diagnostic capability across airports for effective disease control.\u003c/p\u003e\u003cp\u003eFurthermore, hand-held thermal imaging cameras are functional at all airports, but HD thermal cameras are completely absent. The reason could be associated with cost as hand-held camera devices are typically more affordable, portable and easier to operate compared to HD thermal imaging cameras. The cheap and easy to operate nature of hand-held cameras explained their widespread use in the airports. However, their HD versions may be cost-prohibitive or the airports may lack technical support for mounting such tool for health data collection. Keahey [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] demonstrated that hand-held thermal cameras offer effective fever screening in resource-limited settings, despite lower precision compared to HD models. However, Manullang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] caution that absence of HD thermal cameras may compromise early detection due to less accurate temperature readings. While hand-held cameras meet baseline-screening needs, there is however needs to upgrade to HD devices to improve diagnostic accuracy and disease prevention potential.\u003c/p\u003e\u003cp\u003eOn fingerprint scanners, this study found that all airports have functional fingerprint scanners, whereas facial recognition and iris scanners are not available. This availability of fingerprint scanners are cheaper, more established and easier to integrate than advanced biometrics, which demand high-end infrastructure and raise privacy concerns. Ode-Martins [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] report fingerprint systems as preferred biometric tools in many airports due to its cost-benefit advantages. Contrarily, Huang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] argued that lack of facial recognition limits security and disease control and response potentials of the airports, especially as it enables touchless identification. Expanding biometric technologies should balance cost, privacy and operational efficiency for enhanced security and diseases prevention.\u003c/p\u003e\u003cp\u003eFindings from this study showed EHR systems, sensor-based smart cameras (CCTV) and RFID tags were available and functional across all airports. The availability of these systems are foundational for healthcare data management, surveillance and diseases/assert tracking. Hence, prioritized deployment of technology during health emergency can help to further strengthen health surveillance and spread tracking. This position aligns with the Study of Mohamed and Al-Azab [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], who emphasize the roles of these tools in streamlining airport health services and logistics. Contrary to Mohamed and Al-Azab, Ștefan \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] posited that technologies alone are insufficient without integration of comprehensive health protocols into such technological deployment. This shows that consistent use of EHR and RFID indicates effective adoption that support operational integrity of the airports in diseases surveillance.\u003c/p\u003e\u003cp\u003eAdditionally, findings from this study revealed a mixed availability and functionality of Bluetooth Low Energy (BLE) Beacons and GIS hardware. The reason for this variability in deployment may be due to differing technical capacity or budget limitations among airports. Ibrahim \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] link variable in beacon performance to infrastructure disparities. This infrastructure disparities maybe technical capacity, level of personnel\u0026rsquo;s technological-know-how or budget limitations. While disparities existed, Vo \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] call for more uniform adoption of BLE to maximize health data tracking and spatial analysis efficiency. Standardizing BLE and GIS deployment would improve spatial data utility and contact monitoring for effective diseases prevention and control within and outside the airports.\u003c/p\u003e\u003cp\u003eFindings indicated that GPS, Mobile Applications, IoT Sensors, Social Media, Contact Tracing and Data Analytics Tools were universally available across the studied airports. These tools are relatively cost-effective, scalable and supported by global standards in encouraging digital health surveillance. Blišťanov\u0026aacute; \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] highlighted that their role in pandemic responses within the airports are worldwide. This implies that preventing diseases spreads at airport-level required both availability and functionality of these tools and software technologies for data gathering and scalability. On the contrary, Gisond \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and Xiao \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] warned about data privacy and potential misinformation risks from social media reliance. The warning remained a valid point considering the negative impact of misinformation on health emergency responses. The widespread availability of the tools and technologies signals strong readiness for big data adoption in diseases responses, which should be match with ongoing management of data privacy and accuracy applications.\u003c/p\u003e\u003cp\u003eAdditionally, findings revealed that big data tools perform targeted roles that ranges from fever screening (thermal cameras), authentication (biometrics), record management (EHR), surveillance (CCTV), real-time tracking (BLE beacons, GPS), pathogen detection (PCR), health reporting (mobile apps), environmental monitoring (IoT), communication (social media), exposure notification (contact tracing apps) to data analysis (analytics tools). The reason is that each tool is uniquely design, adapted and employed to addresses specific needs for disease prevention, passenger safety and airport operation efficiency. Studies by Chowdhury [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], Alam \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and Ahmed \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] all provided evidence of such multifaceted applications of big data and big data technologies for collection of real-time healthcare data tracking.\u003c/p\u003e\u003cp\u003eContrarily, Troxell and Sprague [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] highlighted the risks of over-dependence on technology and the need for human oversight, as technological tools are prone to errors and cyber threats. Giudice da Silva Cezar and Ma\u0026ccedil;ada [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and Hussain \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] on the other hand, stress digital literacy challenge that limits the application of big data technology, which may result in technological inaccuracies and errors in data collection and applications of such data. Hence, a comprehensive use of big data tools, even though it demonstrated effective, might require integration of human efforts with a balanced human-technology collaboration. This remains an important facet in the adoption and integration of big data into diseases prevention and epidemiological responses within and outside the airport settings.\u003c/p\u003e\u003cp\u003eAlso, the study\u0026rsquo;s findings indicated that \u003cb\u003ed\u003c/b\u003eata generated from the use of big data tools include medical, location, biometrics, contact, pathogen molecular and geographic data used in hazard identification, surveillance, diagnosis, prevention, investigation and contact tracing. Additionally, the adoption of big data technologies were driven by pandemics, compliance and disease risk reduction, while common challenges were privacy concerns, costs and supply limits. Privacy emerged as the most dominant ethical issue experienced in the use of big data tools and technologies. The reason is that pandemics have accelerated demand for data-driven interventions, and there is an equal need for intensifying concerns around privacy and resource allocation.\u003c/p\u003e\u003cp\u003eStudies by Javed \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], Olaboye \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and Igwama \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] all identified these drivers and challenges, explaining that without adoption of big data tools designing interventions for health emergency responses and diseases control may become even more difficult, which may further pose greater threats to public health and healthcare systems. However, Buckman \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] shared a contrary view on data privacy concerns stating that while privacies are important, implementing stringent data privacy rule may reduce the efficiency of public health responses. The study called for a relaxed data privacy rules in health emergencies. Bag \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], on the other hand, suggested that decreasing costs of big data tools might alleviate supply issues. This implies that effective data policies would involve balancing data utility and privacy coupled with cost management. These are vital for sustained big data adoption and use in the prevention of diseases outbreaks and their spreads through international travels.\u003c/p\u003e\u003cp\u003eFindings from this research revealed that Nigerian airports had high rating in big data and big data generating tools availability, functionality, application, personnel sufficiency, competent outbreak handling and operational efficiency when compared to global standards. They demonstrated strong readiness and capacity in managing epidemiological risks using advanced big data technologies. This high rating could be link to institutional commitment and pandemic urgency, which have accelerated technological adoption and capacity building. Amoo [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and Ogunboye [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] reported similar progress in other emerging economies. However, Olayinka [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] argued that quantitative benchmarking may masked the gaps in real-time responsiveness. In summary, however, Nigerian airports are position technologically for diseases prevention and health emergency responses. Hence, strengthening the real-time management and continuous personnel training will optimize outcomes and reduce the spread of diseases through inter-state and international air travels.\u003c/p\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eThe study concludes that disparities in medical technology across airports affect their capacity for effective disease prevention as some airports lack important diagnostic tools. While handheld thermal cameras and fingerprint scanners are widely used due to affordability, advanced technological tools like HD thermal cameras and biometric systems remain limited by cost and privacy concerns. Conversely, digital tools such as Electronic Health Records and GPS are consistently functional, which support robust disease surveillance, risk detection and emergency response. Although uneven adoption of technologies like BLE beacons highlighted gaps in readiness, it could be concluded that the assessed airports have baseline big data tools for the detection and prevention of diseases spreads through air travels. Big data tools play vital and multifaceted roles in managing epidemiological risks, but challenges such as data privacy, technological literacy and resource constraints must be addressed to improve application of available. Also, Nigerian airports showed strong capability and commitment in applying big data technologies to prevention and reduced transmission of epidemic via air travels. It is suggested that continued investment in technology, training and real-time management will further strengthen disease control efforts and reduce epidemic risks from air travels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNo funding was received for this research, as the research was self-funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual Publication: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo dual publication submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAjodo E. U. collected the data and compiled the research paper. Kuti I. A. supervised the research and contributed to improving the structure and content. Ajobo S. assisted in the data collection and analysis. Ndagi U. co-designed the research and facilitated data collection. All authors discussed the results and contributed to the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (WHO, 2015). Health in 2015: from MDGs, millennium development goals to SDGs, sustainable development goals. World health organization. Retrieved from https://Wisnerepidemiology\u0026amp;otsonepage\u0026amp;q, 25th June 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander D. (2018). \u003cem\u003eNatural Disasters\u003c/em\u003e. Routledge. 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Adv Healthc Mater. 2024;13(17):2303923.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlišťanov\u0026aacute; M, Tirpakova M, Brůnov\u0026aacute; Ľ. Overview of safety measures at selected airports during the COVID-19 pandemic. Sustainability. 2021;13(15):8499.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGisondi MA, Barber R, Faust JS, Raja A, Strehlow MC, Westafer LM, Gottlieb M. A deadly infodemic: social media and the power of COVID-19 misinformation. J Med Internet Res. 2022;24(2):e35552.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao X, Borah P, Su Y. The dangers of blind trust: Examining the interplay among social media news use, misinformation identification, and news trust on conspiracy beliefs. Public Underst Sci. 2021;30(8):977\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChowdhury RH. Big data analytics in the field of multifaceted analyses: A study on health care management. 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(2024). \u003cem\u003eAssessment of Constraints to the Implementation of International Health Regulation at the Point of Entry in Lagos Nigeria\u003c/em\u003e (Master's thesis, Kwara State University (Nigeria)).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOgunboye I, Adebayo IPS, Anioke SC, Egwuatu EC, Ajala CF, Awuah SB. (2023). Enhancing Nigeria\u0026rsquo;s health surveillance system: A data-driven approach to epidemic preparedness and response\u0026rsquo;. World J Adv Res Reviews, \u003cem\u003e20\u003c/em\u003e(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlayinka OH. Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness. Int J Sci Res Arch. 2021;4(1):280\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Qualitative assessment, big data, big data tools, prevention, epidemiological diseases, disease spread, airports","lastPublishedDoi":"10.21203/rs.3.rs-7931768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7931768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global spread of epidemiological diseases because of inter-country travels through the airports raises concern on the safety and health of the global community. The 2019 coronavirus outbreak that started from Wuhan China and spread to other parts of the world is a typical example. Efforts toward preventing global epidemiological diseases\u0026rsquo; spread through the international airport had led to the use of big data and big data technologies. This research investigate the role of big data in the management of epidemiological diseases in aviation industry. The study adopted qualitative research approach using checklist and interviews. The qualitative data were obtain through in-person and telephone interview. The data were analysed using thematic and content analysis. Result showed that Sensor-based Smart Cameras (CCTV), computer-based PCR machines, Fingerprint Biometric Identification and Electronic Health Records (EHR) Systems and Bluetooth Low Energy (BLE) Beacons were among the big data collection tools used at the airports. The performance of the airports in term of big data/big data tools availability, functionality and operations was 70%, which showed s high-level of compliance to international best practices by the airports. Top concerns were data privacy, high cost of big data tools and technologies, and limited supplies. The study concluded that there is high-level of utilization and compliance to standards in the ways big data were use among the airport, with consensus on the usefulness of these tools in diseases detection and prevention at airports. It recommends developing robust data governance policies that prioritize the protection of passengers and employees\u0026rsquo; data while ensuring compliance with regulatory standards. This ensure that travelers safety and guarantees the mitigation of diseases\u0026rsquo; spread through the airports.\u003c/p\u003e","manuscriptTitle":"Qualitative Assessment of the Use of Big Data and Big Data Tools in the Prevention of Epidemiological Diseases’ Spread Through Airports","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 05:24:30","doi":"10.21203/rs.3.rs-7931768/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10754a51-88a8-4483-ad40-ec952b0071ad","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T11:39:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 05:24:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7931768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7931768","identity":"rs-7931768","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}