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Some of the key questions of interest during the COVID-19 pandemic (and alloutbreaks) include: where did the disease start, how is it spreading, who is atrisk, and how to control the spread. There are a large number of complexfactors driving the spread of pandemics, and, as a result, multiple modelingtechniques play an increasingly important role in shaping public policy anddecision making. As different countries and regions go through phases of thepandemic, the questions and data availability also changes. Especially ofinterest is aligning model development and data collection to support responseefforts at each stage of the pandemic. The COVID-19 pandemic has beenunprecedented in terms of real-time collection and dissemination of a number ofdiverse datasets, ranging from disease outcomes, to mobility, behaviors, andsocio-economic factors. The data sets have been critical from the perspectiveof disease modeling and analytics to support policymakers in real-time. In thisoverview article, we survey the data landscape around COVID-19, with a focus onhow such datasets have aided modeling and response through different stages sofar in the pandemic. We also discuss some of the current challenges and theneeds that will arise as we plan our way out of the pandemic.

Data-driven modeling for different stages of pandemic response