  Some of the key questions of interest during the COVID-19 pandemic (and all
outbreaks) include: where did the disease start, how is it spreading, who is at
risk, and how to control the spread. There are a large number of complex
factors driving the spread of pandemics, and, as a result, multiple modeling
techniques play an increasingly important role in shaping public policy and
decision making. As different countries and regions go through phases of the
pandemic, the questions and data availability also changes. Especially of
interest is aligning model development and data collection to support response
efforts at each stage of the pandemic. The COVID-19 pandemic has been
unprecedented in terms of real-time collection and dissemination of a number of
diverse datasets, ranging from disease outcomes, to mobility, behaviors, and
socio-economic factors. The data sets have been critical from the perspective
of disease modeling and analytics to support policymakers in real-time. In this
overview article, we survey the data landscape around COVID-19, with a focus on
how such datasets have aided modeling and response through different stages so
far in the pandemic. We also discuss some of the current challenges and the
needs that will arise as we plan our way out of the pandemic.
