Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
Department of Mathematics, University of Trento, Trento, Italy.
Front Public Health. 2024 Aug 21;12:1430920. doi: 10.3389/fpubh.2024.1430920. eCollection 2024.
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.
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.
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.
Nowcasting augments epidemic awareness, empowering better informed public health responses.
时变繁殖数 R 是传染病爆发期间情境感知的关键变量；然而，感染与病例报告之间的延迟阻碍了其在实时环境下的准确估计。为了解决这个问题，已经提出了许多实时预测方法，利用数据整合延迟的可用信息。
在这项工作中，我们通过定量评估其在意大利 COVID-19 大流行 18 个月期间的使用性能，以标准的 R 估计方法为对照，对实时预测算法进行了回顾性验证。
实时预测显著降低了 R 估计的中位数延迟，从 13 天减少到 8 天，同时提高了准确性。此外，它还可以提前 6 到 23 天检测到疫情增长期。
实时预测增强了对传染病的认识，使公共卫生应对措施更加明智。
