ABSTRACT
Early in 2020, the COVID-19 pandemic emerged as a global public health concern requiring urgent attention, concerted efforts and intervention to avoid catastrophe. This necessitated optimal use of fast-emerging data to be analysed to draw out inferences that would shape our response. World Health Organisation (WHO) called this pandemic an infodemic where data played a crucial role. This paper reviews how data from varied sources and different types helped delay the outbreak, limit the spread, initiate social and public health measures, decide treatment regimes, optimise healthcare infrastructure and human resources and helped to initiate a multipronged strategy with emerging evidence for further course correction as the world progressed through the pandemic. The classical mathematical tools, i.e., Susceptible-Infected-Recovered (SIR) model and its variants, were the primary analytical techniques utilised to analyse such data. However, newer data analytical techniques utilising artificial intelligence and machine learning, were also extensively used. These techniques have the capability to handle large quantities of data and develop prediction models of various emerging situations that offer foreknowledge for policymakers and provide solutions. Data Science has witnessed a leap in the past few years, and the way it helped shape our response to this pandemic is a testimony to the promise that it holds for humankind.