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

Abstract

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.