Big Data, Big Data Analytics, and Policymaking During a Global Pandemic

This chapter discusses the transformative role of Big Data and Big Data analytics in informing policymaking during global pandemics, focusing on the COVID-19 crisis. Exploring various applications of Big Data analytics, including sentiment analysis, predictive modelling, and behavioural analysis, the chapter explains how high-frequency data provides unprecedented insights into human behaviour, attitudes toward government responses, and disease dynamics. The chapter begins by introducing the concept of Big Data and its advantages over traditional survey data, emphasising the need for real-time information to guide policymakers effectively. It illustrates how Big Data analytics enables the early detection and tracking of outbreaks, facilitates predictive modelling for disease spread, advances vaccine development and distribution, and optimises healthcare resource allocation. Drawing on examples from the COVID-19 pandemic, the chapter highlights the invaluable contributions of Big Data analytics in understanding the virus’s transmission patterns, assessing intervention effectiveness, and identifying vulnerable populations. It underscores the role of machine learning algorithms in analysing vast datasets to inform targeted public health messaging and mitigate misinformation. Furthermore, the chapter outlines how Big Data empowers policymakers to monitor public sentiment, identify regions with low compliance rates, and assess the economic impact of the pandemic. By leveraging real-time insights from diverse data sources, policymakers can make data-driven decisions, respond swiftly to evolving challenges, and save lives.