Application of big data and artificial intelligence in epidemic surveillance and containment

Jiao, Zengtao; Ji, Hanran; Yan, Jun; Qi, Xiaopeng

doi:10.1016/j.imed.2022.10.003

Cited by 14 publications

(11 citation statements)

References 96 publications

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“…However, EBS systems should be vigilant regarding two important challenges, namely, strengthening ethical issues and data protection and ensuring intellectual property protection for researchers and developers [ 10 ], and should take advantage of the experience and knowledge of participatory surveillance systems applied to ILI [ 22 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…These expectations are in line with a previous qualitative study with artificial intelligence (AI) experts [ 36 ] that validated these opportunities and identified the stakes to strengthen the legal framework and collaboration between AI experts and PH/AH practitioners. To increase the acceptability of new methods concerning big data and AI, Zengtao recommended grounding technology developments on practitioners’ problems and guaranteeing the intellectual property of researchers [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

“…), and aggregated internet search trends (e.g., Google search trends) [ 8 , 9 ]. The availability of new information sources and methods to explore large volumes of data leads to new challenges in managing, analysing and interpreting these data flows [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance

Bouyer,

Thiongane,

Hobeika

et al. 2024

BMC Public Health

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Background European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. Methods We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. Results Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. Conclusions The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance

Bouyer,

Thiongane,

Hobeika

et al. 2024

BMC Public Health

View full text Add to dashboard Cite

show abstract

“…These expectations are in line with a previous qualitative study with AI experts (30) that validated these opportunities and identi ed the stakes to strengthen the legal framework and the collaborations between Arti cial Intelligence (AI) experts and PH/AH practitioners. To increase the acceptability of new methods concerning big data and AI, Zengtao recommended grounding technology developments on practitioners' problems, and to guarantee the intellectual property of researchers (10).…”

Section: Strengthening the Analyticalmentioning

confidence: 99%

Epidemic Intelligence in Europe: a users’ needs perspective to foster innovation in digital health surveillance

Bouyer,

Thiongane,

Hobeika

et al. 2023

Preprint

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Background European Epidemic Intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyze these data sources are constantly increasing. Our aim was which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and emergence of new pathogens. Methods We conducted a qualitative study to identify the needs for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Center for Disease Control (ECDC). We adopted a step-wise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, to validate and further define practitioners’ problems to offer solutions. We characterized their EI activities, their professional logics, and the desired changes in their activities. Results Our analysis highlights that EI practitioners wished to collectively review their EI strategy in order to enhance their preparedness for emerging infectious diseases, adapt their routine to manage an increasing amount of data and to have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariates sets for epidemiological assessments of national EI. The set of solutions, identified to facilitate risk detection and risk assessment, included visualization, text-mining, predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for a partial rather than a full automation of analyses in order to keep control over the data, inputs and adapt parameters to versatile objectives and characteristics. Conclusions Overall, the set of solutions had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on a harmonization between agencies and sectors, while keeping flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

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“…In the future, big data information services will continue to develop into personalized management services and expand into the healthcare industry. Big data and AI technologies play an important role in epidemic prevention and control ( 10 ). The mobile health (mHealth) app changes personal health and lifestyle based on new information and communication technologies and the IoT and enables user behavior change and disease management through data collection and analysis for clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Utilization of smart devices and the evolution of customized healthcare services focusing on big data: a systematic review

Son,

Kwon

2024

mHealth

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Background Currently, smart devices can prevent diseases by continuously collecting user information and providing health-related feedback. Smart devices big data provide personalized, faster, and more accurate health care. By examining existing studies, we suggest a new healthcare evolution and health promotion through information technology (IT) convergence. A big data systematic review examined the evolution of new health care and their potential for health promotion by monitoring physical activities, preventing diseases, and analyzing health data smart devices. Methods Therefore, this evaluates whether a new healthcare industry combining smart devices and big-data-based customized health care services can promote health. This study searched PubMed, Google Scholar, Scopus, and Research Information Sharing Service (RISS) for keywords related to big data, smart devices, healthcare, customized health services, health apps, and mobile health. This study comprised 43 of 453 publications from 2007 to 2023. Among them, a total of 43 articles were successfully completed in this study using the PRISMA flowchart in the final stage. Results Smart devices centered on big data enable personalized health care, and app technologies that promote well-being to prepare for aging society have many applications in clinical, prevention, public health, and rehabilitation settings. Smart devices and tailored healthcare services using big data to inform individuals about exercise, health status, diagnosis, and health information will expand into major sectors. By reviewing previous studies, the convergence of the IT technology field, which allows you to easily identify individual health and receive faster and more accurate medical services through customized health care services, has future-oriented values as, new health care services evolve. The systematic review of big data herein can monitor physical activity and prevent diseases using smart devices, thus promoting a healthy lifestyle. Conclusions Smart devices that analyze data to provide personal exercise and health conditions, checkups, and information, are making our lives easier. The information service using big data will continue to evolve into a personalized management service and provide basic healthcare data as it grows into an expected industry in the future.

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Application of big data and artificial intelligence in epidemic surveillance and containment

Cited by 14 publications

References 96 publications

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance

Epidemic Intelligence in Europe: a users’ needs perspective to foster innovation in digital health surveillance

Utilization of smart devices and the evolution of customized healthcare services focusing on big data: a systematic review

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