  COVID-19 pandemic has an unprecedented impact all over the world since early
2020. During this public health crisis, reliable forecasting of the disease
becomes critical for resource allocation and administrative planning. The
results from compartmental models such as SIR and SEIR are popularly referred
by CDC and news media. With more and more COVID-19 data becoming available, we
examine the following question: Can a direct data-driven approach without
modeling the disease spreading dynamics outperform the well referred
compartmental models and their variants? In this paper, we show the
possibility. It is observed that as COVID-19 spreads at different speed and
scale in different geographic regions, it is highly likely that similar
progression patterns are shared among these regions within different time
periods. This intuition lead us to develop a new neural forecasting model,
called Attention Crossing Time Series (\textbf{ACTS}), that makes forecasts via
comparing patterns across time series obtained from multiple regions. The
attention mechanism originally developed for natural language processing can be
leveraged and generalized to materialize this idea. Among 13 out of 18 testings
including forecasting newly confirmed cases, hospitalizations and deaths,
\textbf{ACTS} outperforms all the leading COVID-19 forecasters highlighted by
CDC.
