  The ongoing COVID-19 pandemic continues to affect communities around the
world. To date, almost 6 million people have died as a consequence of COVID-19,
and more than one-quarter of a billion people are estimated to have been
infected worldwide. The design of appropriate and timely mitigation strategies
to curb the effects of this and future disease outbreaks requires close
monitoring of their spatio-temporal trajectories. We present machine learning
methods to anticipate sharp increases in COVID-19 activity in US counties in
real-time. Our methods leverage Internet-based digital traces -- e.g.,
disease-related Internet search activity from the general population and
clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories
from neighboring locations -- to monitor potential changes in population-level
health trends. Motivated by the need for finer spatial-resolution
epidemiological insights to improve local decision-making, we build upon
previous retrospective research efforts originally conceived at the state level
and in the early months of the pandemic. Our methods -- tested in real-time and
in an out-of-sample manner on a subset of 97 counties distributed across the US
-- frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before
the onset of local outbreaks (defined as the time when the effective
reproduction number $R_t$ becomes larger than 1 consistently). Given the
continued emergence of COVID-19 variants of concern -- such as the most recent
one, Omicron -- and the fact that multiple countries have not had full access
to vaccines, the framework we present, while conceived for the county-level in
the US, could be helpful in countries where similar data sources are available.
