Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Relying on Google search interest data across 207 countries and territories, we demonstrate the capacity of publicly-available, real-time data to anticipate COVID-19 cases; evaluate the economic, mental health, and social impacts of containment policies; and identify demand for (mis)information about COVID-19 vaccines. We show that: (1) search interest in COVID-specific symptoms can anticipate rising COVID-19 cases across both high- and low-income settings; (2) countries with more restrictive containment policies experienced larger socio-economic externalities; in addition, lower-income countries experienced less searches for unemployment, but more pronounced mental health externalities; and (3) high vaccination rates are associated with strong demand for information about vaccine appointments and side effects; in some settings, high interest in misinformation search terms is associated with low vaccination rates. Overall, the results demonstrate that real-time search interest data can be a valuable tool for both high- and low-income countries to inform policies across multiple stages of the pandemic.