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利用动态因果模型验证先前的预测,并对英国 COVID-19 疫情的长期影响做出 12 个月的预测。

Using a Dynamic Causal Model to validate previous predictions and offer a 12-month forecast of the long-term effects of the COVID-19 epidemic in the UK.

机构信息

Retired, Axminster, United Kingdom.

Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.

出版信息

Front Public Health. 2023 Jan 6;10:1108886. doi: 10.3389/fpubh.2022.1108886. eCollection 2022.

Abstract

BACKGROUND

Predicting the future UK COVID-19 epidemic provides a baseline of a vaccine-only mitigation policy from which to judge the effects of additional public health interventions. A previous 12-month prediction of the size of the epidemic to October 2022 underestimated its sequelae by a fifth. This analysis seeks to explain the reasons for the underestimation before offering new long-term predictions.

METHODS

A Dynamic Causal Model was used to identify changes in COVID-19 transmissibility and the public's behavioral response in the 12-months to October 2022. The model was then used to predict the future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2023.

FINDINGS

The model estimated that the secondary attack rate increased from 0.4 to 0.5, the latent period shortened from 2.7 to 2.6 and the incubation period shortened from 2.0 to 1.95 days between October 2021 and October 2022. During this time the model also estimated that antibody immunity waned from 177 to 160 days and T-cell immunity from 205 to 180 days. This increase in transmissibility was associated with a reduction in pathogenicity with the proportion of infections developing acute respiratory distress syndrome falling for 6-2% in the same twelve-month period. Despite the wave of infections, the public response was to increase the tendency to expose themselves to a high-risk environment (e.g., leaving home) each day from 33-58% in the same period.The predictions for October 2023 indicate a wave of infections three times larger this coming year than last year with significant health and economic consequences such as 120,000 additional COVID-19 related deaths, 800,000 additional hospital admissions and 3.5 million people suffering acute-post-COVID-19 syndrome lasting more than 12 weeks.

INTERPRETATION

The increase in transmissibility together with the public's response provide plausible explanations for why the model underestimated the 12-month predictions to October 2022. The 2023 projection could well-underestimate the predicted substantial next wave of COVID-19 infection. Vaccination alone will not control the epidemic. The UK COVID-19 epidemic is not over. The results call for investment in precautionary public health interventions.

摘要

背景

预测未来英国的 COVID-19 疫情,可为仅依靠疫苗的缓解政策提供基线,据此可判断额外公共卫生干预措施的效果。此前对 2022 年 10 月前 12 个月疫情规模的预测低估了其后遗症的五分之一。本分析旨在在提供新的长期预测之前,解释预测低估的原因。

方法

使用动态因果模型来识别 2021 年 10 月至 2022 年 10 月 12 个月期间 COVID-19 传染性变化和公众行为反应。然后,该模型用于预测 2023 年 12 月前 12 个月内感染、长 COVID、住院和死亡的未来趋势。

结果

模型估计,2021 年 10 月至 2022 年 10 月期间,二次攻击率从 0.4 增加到 0.5,潜伏期从 2.7 缩短至 2.6 天,潜伏期从 2.0 缩短至 1.95 天。在此期间,模型还估计抗体免疫从 177 天降至 160 天,T 细胞免疫从 205 天降至 180 天。这种传染性的增加与致病性的降低有关,在同一 12 个月内,出现急性呼吸窘迫综合征的感染比例从 6%降至 2%。尽管感染浪潮汹涌,但公众的反应是每天增加暴露于高风险环境(例如离家)的倾向,从同一时期的 33-58%增加。对 2023 年 10 月的预测表明,今年的感染浪潮将比去年大三倍,带来严重的健康和经济后果,例如 COVID-19 相关死亡人数增加 12 万人,住院人数增加 80 万,350 万人患急性长 COVID-19 综合征超过 12 周。

解释

传染性的增加加上公众的反应,为模型为何低估了截至 2022 年 10 月的 12 个月预测提供了合理的解释。2023 年的预测很可能低估了预测的下一波 COVID-19 感染。仅接种疫苗无法控制疫情。英国的 COVID-19 疫情尚未结束。结果呼吁投资于预防性公共卫生干预措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a609/9853371/5a05ff5ee93b/fpubh-10-1108886-g0001.jpg

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