Imagen de Portada

Beyond the outbreak: a review of big data analytics in proactive infectious disease prevention for risk mitigation for COVID-19

Guardado en:
Publicado en:Journal of Big Data vol. 12, no. 1 (Jul 2025), p. 185
Autor principal: Nuha, Nurun
Otros Autores: Ali Pitchay, Sakinah, Ab Halim, Azni Haslizan, Sahbudin, Murtadha Arif Bin, Sahbudin, Ilfita
Publicado:
Springer Nature B.V.
Materias:
Infectious diseases
Data analysis
Health surveillance
Malaria
Scientific visualization
Big Data
Support vector machines
Health care
Prediction models
Public health
Disease control
Telemedicine
Tracking systems
Laboratories
Regression analysis
Literature reviews
Deep learning
Machine learning
Real time
Decision trees
Tuberculosis
Reliability
Surveillance
Medical diagnosis
Disease
Mapping
Data quality
Delayed
Social media
Predictions
Pandemics
COVID-19
Generalizability
Tracking
Responses
Disease prevention
Data processing
Visualization
Health services
Time series
Epidemiology
Measles
Decision making
Mitigation
Medical decision making
Clinical decision making
Forecasting
Epidemics
Machinery
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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.
ISSN:2196-1115
DOI:10.1186/s40537-025-01245-z
Fuente:ABI/INFORM Global