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回顾性评估全球 COVID-19 传播趋势的实时估计和死亡率预测。

Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts.

机构信息

MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.

NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency, London, United Kingdom.

出版信息

PLoS One. 2023 Oct 18;18(10):e0286199. doi: 10.1371/journal.pone.0286199. eCollection 2023.

Abstract

Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

摘要

自 2020 年 3 月 8 日至撰写本文之时,我们一直在为所有有持续传播证据的国家提供 SARS-CoV-2 传染性的实时周报估计和 COVID-19 死亡预测,并在线共享。我们还开发了一种新的启发式方法,将每周的传染性估计值结合起来,以产生 4 周预测。在此,我们回顾评估了 2020 年 3 月 8 日至 11 月 29 日期间 81 个国家的预测。我们使用相对误差、覆盖概率以及与零模型的比较,对实时产生的预测进行了稳健性评估。在本研究涵盖的 39 周期间,短期和中期预测都很好地捕捉了不同 COVID-19 感染波次的疫情轨迹,在预测期内的相对误差较小。该模型具有良好的校准能力,在 1 周和 4 周的前瞻性预测中,分别有 56.3%和 45.6%的观测值落在 50%可信区间内。对我们模型的回顾性评估表明,使用常规疾病监测数据校准的简单传播模型可以可靠地捕捉多个国家的疫情轨迹。随着各国继续放松公共卫生措施,中期预测可以与 COVID-19 死亡率的短期预测结合使用,作为一种有用的规划工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d23/10584190/ed7b1e4d36e2/pone.0286199.g001.jpg

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