Beyond the outbreak: a review of big data analytics in proactive infectious disease prevention for risk mitigation for COVID-19
محفوظ في:
| الحاوية / القاعدة: | Journal of Big Data vol. 12, no. 1 (Jul 2025), p. 185 |
|---|---|
| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , , |
| منشور في: |
Springer Nature B.V.
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text Full Text - PDF |
| الوسوم: |
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3233582257 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2196-1115 | ||
| 024 | 7 | |a 10.1186/s40537-025-01245-z |2 doi | |
| 035 | |a 3233582257 | ||
| 045 | 2 | |b d20250701 |b d20250731 | |
| 100 | 1 | |a Nuha, Nurun |u Universiti Sains Islam Malaysia, Faculty of Science and Technology, Bandar Baru Nilai, Malaysia (GRID:grid.462995.5) (ISNI:0000 0001 2218 9236) | |
| 245 | 1 | |a Beyond the outbreak: a review of big data analytics in proactive infectious disease prevention for risk mitigation for COVID-19 | |
| 260 | |b Springer Nature B.V. |c Jul 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The World Health Organisation (WHO) has identified infectious diseases, particularly COVID-19, tuberculosis, malaria, and measles, as significant global health challenges in the past 5 years. The COVID-19 pandemic exposed critical limitations in traditional disease tracking systems, such as the lack of integrated data visualization, co-monitoring, and real-time analytics, leading to delayed and often ineffective public health responses. In this context, Big Data Analytics (BDA) offers significant potential for improving infectious disease mitigation through predictive modelling, mapping, tracking, and real-time monitoring. This study systematically reviews the role of BDA in monitoring and predicting epidemic and pandemic infections using the PRISMA methodology and quality appraisal techniques to provide comprehensive insights into its healthcare applications. From an initial pool of 846 articles from Scopus, PubMed, Science Direct, IEEE, ProQuest, and Springer, 30 high-quality studies were selected for in-depth analysis. The review identifies four key predictive models—epidemiological, time series, machine learning, and deep learning—and seven analytical techniques, including SIR, SEIR, regression analysis, random forest, support vector machines, auto-regressive methods, and deep learning. BDA supports infectious disease control by processing diverse healthcare data and leveraging technologies like IoT and social media to enhance diagnosis, clinical decision-making, and surveillance. However, a key limitation is predictive models’ limited reliability and generalizability in real-world settings, mainly due to low-quality, noisy, and incomplete data. For instance, during early COVID-19 phases, inconsistent case reporting hindered accurate forecasting and timely response efforts. | |
| 653 | |a Infectious diseases | ||
| 653 | |a Data analysis | ||
| 653 | |a Health surveillance | ||
| 653 | |a Malaria | ||
| 653 | |a Scientific visualization | ||
| 653 | |a Big Data | ||
| 653 | |a Support vector machines | ||
| 653 | |a Health care | ||
| 653 | |a Prediction models | ||
| 653 | |a Public health | ||
| 653 | |a Disease control | ||
| 653 | |a Telemedicine | ||
| 653 | |a Tracking systems | ||
| 653 | |a Laboratories | ||
| 653 | |a Regression analysis | ||
| 653 | |a Literature reviews | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Real time | ||
| 653 | |a Decision trees | ||
| 653 | |a Tuberculosis | ||
| 653 | |a Reliability | ||
| 653 | |a Surveillance | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Disease | ||
| 653 | |a Mapping | ||
| 653 | |a Data quality | ||
| 653 | |a Delayed | ||
| 653 | |a Social media | ||
| 653 | |a Predictions | ||
| 653 | |a Pandemics | ||
| 653 | |a COVID-19 | ||
| 653 | |a Generalizability | ||
| 653 | |a Tracking | ||
| 653 | |a Responses | ||
| 653 | |a Disease prevention | ||
| 653 | |a Data processing | ||
| 653 | |a Visualization | ||
| 653 | |a Health services | ||
| 653 | |a Time series | ||
| 653 | |a Epidemiology | ||
| 653 | |a Measles | ||
| 653 | |a Decision making | ||
| 653 | |a Mitigation | ||
| 653 | |a Medical decision making | ||
| 653 | |a Clinical decision making | ||
| 653 | |a Forecasting | ||
| 653 | |a Epidemics | ||
| 653 | |a Machinery | ||
| 700 | 1 | |a Ali Pitchay, Sakinah |u Universiti Sains Islam Malaysia, Faculty of Science and Technology, Bandar Baru Nilai, Malaysia (GRID:grid.462995.5) (ISNI:0000 0001 2218 9236); CyberSecurity and Systems (CSS) Research Unit, Faculty of Science and Technology, Bandar Baru Nilai, Malaysia (GRID:grid.462995.5) | |
| 700 | 1 | |a Ab Halim, Azni Haslizan |u Universiti Sains Islam Malaysia, Faculty of Science and Technology, Bandar Baru Nilai, Malaysia (GRID:grid.462995.5) (ISNI:0000 0001 2218 9236); CyberSecurity and Systems (CSS) Research Unit, Faculty of Science and Technology, Bandar Baru Nilai, Malaysia (GRID:grid.462995.5) | |
| 700 | 1 | |a Sahbudin, Murtadha Arif Bin |u Universiti Brunei Darussalam, Institute of Applied Data Analytics, Gadong, Brunei (GRID:grid.440600.6) (ISNI:0000 0001 2170 1621); Universiti Brunei Darussalam, School of Digital Science, Gadong, Brunei (GRID:grid.440600.6) (ISNI:0000 0001 2170 1621) | |
| 700 | 1 | |a Sahbudin, Ilfita |u University of Birmingham, Rheumatology Research Group, Institute of Inflammation and Ageing, Birmingham, UK (GRID:grid.6572.6) (ISNI:0000 0004 1936 7486) | |
| 773 | 0 | |t Journal of Big Data |g vol. 12, no. 1 (Jul 2025), p. 185 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233582257/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3233582257/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233582257/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |