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非药物干预对英国 COVID-19 病例、死亡和医院服务需求的影响:一项建模研究。

Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study.

机构信息

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

出版信息

Lancet Public Health. 2020 Jul;5(7):e375-e385. doi: 10.1016/S2468-2667(20)30133-X. Epub 2020 Jun 2.

DOI:10.1016/S2468-2667(20)30133-X
PMID:32502389
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7266572/
Abstract

BACKGROUND

Non-pharmaceutical interventions have been implemented to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been crucial to support evidence-based policy making during the early stages of the epidemic. This study assesses the potential impact of different control measures for mitigating the burden of COVID-19 in the UK.

METHODS

We used a stochastic age-structured transmission model to explore a range of intervention scenarios, tracking 66·4 million people aggregated to 186 county-level administrative units in England, Wales, Scotland, and Northern Ireland. The four base interventions modelled were school closures, physical distancing, shielding of people aged 70 years or older, and self-isolation of symptomatic cases. We also modelled the combination of these interventions, as well as a programme of intensive interventions with phased lockdown-type restrictions that substantially limited contacts outside of the home for repeated periods. We simulated different triggers for the introduction of interventions, and estimated the impact of varying adherence to interventions across counties. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (ie, admission to the intensive care units [ICU]) treatment, and deaths, and compared the effect of each intervention on the basic reproduction number, R.

FINDINGS

We projected a median unmitigated burden of 23 million (95% prediction interval 13-30) clinical cases and 350 000 deaths (170 000-480 000) due to COVID-19 in the UK by December, 2021. We found that the four base interventions were each likely to decrease R, but not sufficiently to prevent ICU demand from exceeding health service capacity. The combined intervention was more effective at reducing R, but only lockdown periods were sufficient to bring R near or below 1; the most stringent lockdown scenario resulted in a projected 120 000 cases (46 000-700 000) and 50 000 deaths (9300-160 000). Intensive interventions with lockdown periods would need to be in place for a large proportion of the coming year to prevent health-care demand exceeding availability.

INTERPRETATION

The characteristics of SARS-CoV-2 mean that extreme measures are probably required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs.

FUNDING

Medical Research Council.

摘要

背景

为了降低严重急性呼吸综合征冠状病毒 2 型(SARS-CoV-2)在英国的传播,已实施非药物干预措施。预测未减轻的流行规模和不同控制措施的潜在效果对于支持大流行早期的循证决策至关重要。本研究评估了减轻英国 COVID-19 负担的不同控制措施的潜在影响。

方法

我们使用随机年龄结构传播模型来探索一系列干预方案,追踪英格兰、威尔士、苏格兰和北爱尔兰的 6640 万人,将其聚集到 186 个县级行政单位。建模的四个基本干预措施是学校关闭、保持身体距离、保护 70 岁或以上的人、以及对有症状的病例进行自我隔离。我们还对这些干预措施的组合以及一个包括分阶段封锁式限制的强化干预方案进行了建模,该方案多次限制了家庭以外的接触。我们模拟了不同干预措施的引入触发因素,并估计了不同县之间干预措施遵守情况的差异对其产生的影响。对于每种情况,我们都预测了随时间推移的新发病例、需要住院和重症监护(即 ICU 治疗)的患者以及死亡人数,并比较了每种干预措施对基本再生数 R 的影响。

结果

我们预计,到 2021 年 12 月,英国未经干预的 COVID-19 临床病例将达到 2300 万例(95%预测区间为 1300 万至 3000 万),死亡人数将达到 35 万例(17 万至 48 万)。我们发现,四项基本干预措施都有可能降低 R,但不足以防止 ICU 需求超过卫生服务能力。联合干预措施在降低 R 方面更为有效,但只有封锁期才能使 R 接近或低于 1;最严格的封锁方案预计会导致 12 万例病例(4.6 万至 70 万)和 5 万例死亡(9300 至 16 万)。为了防止医疗保健需求超过供应,可能需要在未来一年的大部分时间内实施密集的干预措施和封锁期。

解释

SARS-CoV-2 的特征意味着,要控制疫情并防止大量死亡和 ICU 病床需求过剩,可能需要采取极端措施。

资金

医学研究理事会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c091/7327523/92a8d6143ace/gr4.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c091/7327523/dd3ee36b09ef/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c091/7327523/071ddb9b07c7/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c091/7327523/67959a1390ae/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c091/7327523/92a8d6143ace/gr4.jpg

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