Massimo Fabiani: Department of Infectious Diseases, Istituto Superiore di Sanit��, Rome, Italy.
Antonino Bella: Department of Infectious Diseases, Istituto Superiore di Sanit��, Rome, Italy.
Flavia Riccardo: Department of Infectious Diseases, Istituto Superiore di Sanit��, Rome, Italy.
Patrizio Pezzotti: Department of Infectious Diseases, Istituto Superiore di Sanit��, Rome, Italy.
Marco Ajelli: Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, United States.
Stefano Merler: Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
Background: The time-varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks; however, delays between infection and reporting of cases hinder its accurate estimation in real-time. A number of nowcasting methods, leveraging available information on data consolidation delays, have been proposed to mitigate this problem. Methods: In this work, we retrospectively validate the use of a nowcasting algorithm during 18 months of the COVID-19 pandemic in Italy by quantitatively assessing its performance against standard methods for the estimation of R. Results: Nowcasting significantly reduced the median lag in the estimation of R from 13 to 8 days, while concurrently enhancing accuracy. Furthermore, it allowed the detection of periods of epidemic growth with a lead of between 6 and 23 days. Conclusions: Nowcasting augments epidemic awareness, empowering better informed public health responses.
