Big Data Analytics in Healthcare: Applications for Pandemic Forecastin
DOI:
https://doi.org/10.15662/IJARCST.2020.0301002Keywords:
Big Data, Healthcare Analytics, Pandemic Forecasting, Disease Surveillance, Predictive Modeling, Machine Learning, Real-Time Analytics, Electronic Health Records, Syndromic Surveillance, Pre-2019 LiteratureAbstract
Big data analytics has revolutionized many industries, and in healthcare, it offers pivotal capabilities— especially in the context of pandemic forecasting. This study explores the role of diverse big data sources—electronic health records, social media feeds, syndromic surveillance data, mobile location data, and environmental metrics—in forecasting pandemics. The paper’s objective is to synthesize pre-2019 methodologies and evidence supporting predictive modeling for early detection, trend analysis, and resource planning. We review how machine learning techniques (including regression, decision trees, clustering, and time-series analysis) harness volume, velocity, and variety of healthcare data to create accurate predictions of disease outbreaks. We also examine system architecture workflows that preprocess, integrate, train, and evaluate models for actionable insights. Key findings from literature before 2019 reveal that real-time analytics significantly enhances outbreak lead time, improves geographical granularity in forecasts, and supports efficient allocation of medical resources. Advantages include improved timeliness, scalability, and cost-effectiveness; disadvantages include data heterogeneity, privacy risks, and algorithmic bias. The results and discussion section consolidates empirical evidence of performance metrics (e.g., accuracy, lead-time gains), addresses implementation challenges, and highlights implications for public health policy. Finally, the paper concludes by reaffirming the crucial role of big data analytics in pandemic preparedness and forecasting, and proposes several avenues for future research—such as integrating genomic surveillance, enhancing data interoperability, leveraging deep learning approaches, and strengthening ethical frameworks. Together, this work underscores how pre-2019 advances in data analytics provide a foundation for more resilient pandemic forecasting systems.
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