  In real-time forecasting in public health, data collection is a non-trivial
and demanding task. Often after initially released, it undergoes several
revisions later (maybe due to human or technical constraints) - as a result, it
may take weeks until the data reaches to a stable value. This so-called
'backfill' phenomenon and its effect on model performance has been barely
studied in the prior literature. In this paper, we introduce the multi-variate
backfill problem using COVID-19 as the motivating example. We construct a
detailed dataset composed of relevant signals over the past year of the
pandemic. We then systematically characterize several patterns in backfill
dynamics and leverage our observations for formulating a novel problem and
neural framework Back2Future that aims to refines a given model's predictions
in real-time. Our extensive experiments demonstrate that our method refines the
performance of top models for COVID-19 forecasting, in contrast to non-trivial
baselines, yielding 18% improvement over baselines, enabling us obtain a new
SOTA performance. In addition, we show that our model improves model evaluation
too; hence policy-makers can better understand the true accuracy of forecasting
models in real-time.
