  Epidemic forecasts are only as good as the accuracy of epidemic measurements.
Is epidemic data, particularly COVID-19 epidemic data, clean and devoid of
noise? Common sense implies the negative answer. While we cannot evaluate the
cleanliness of the COVID-19 epidemic data in a holistic fashion, we can assess
the data for the presence of reporting delays. In our work, through the
analysis of the first COVID-19 wave, we find substantial reporting delays in
the published epidemic data. Motivated by the desire to enhance epidemic
forecasts, we develop a statistical framework to detect, uncover, and remove
reporting delays in the infectious, recovered, and deceased epidemic time
series. Our framework can uncover and analyze reporting delays in 8 regions
significantly affected by the first COVID-19 wave. Further, we demonstrate that
removing reporting delays from epidemic data using our statistical framework
may decrease the error in epidemic forecasts. While our statistical framework
can be used in combination with any epidemic forecast method that intakes
infectious, recovered, and deceased data, to make a basic assessment, we
employed the classical SIRD epidemic model. Our results indicate that the
removal of reporting delays from the epidemic data may decrease the forecast
error by up to 50. We anticipate that our framework will be indispensable in
the analysis of novel COVID-19 strains and other existing or novel infectious
diseases.
