通过实时预测繁殖数来提高情境意识。
Increasing situational awareness through nowcasting of the reproduction number.
机构信息
Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
Department of Mathematics, University of Trento, Trento, Italy.
出版信息
Front Public Health. 2024 Aug 21;12:1430920. doi: 10.3389/fpubh.2024.1430920. eCollection 2024.
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.
背景
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。
方法
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。
结果
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。
结论
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。
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