Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
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DOI：
10.1371/journal.pmed.1003189
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发表时间：
2020-07-01
期刊：
影响因子：
15.8
通讯作者：
Riou, Julien
中科院分区：
文献类型：
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作者：
Hauser, Anthony;Counotte, Michel J.;Riou, Julien
作者简介：为什么要做这项研究？严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）感染死亡率的可靠估计是了解临床预后，规划医疗能力和流行病预测所必需的。病例死亡率（CFR），即在特定时间点报告的死亡人数除以报告的病例数，是最常用的指标，但它是SARS-CoV-2感染死亡率的偏倚指标。症状性病死率（sCFR）和总体感染病死率（IFR）是具有临床和公共卫生相关性的死亡率替代指标，应在不同地理位置进一步研究。研究人员做了什么，发现了什么？我们开发了一个数学模型，描述了SARS-CoV-2流行期间的感染传播和死亡。该模型考虑到感染和死亡之间的延迟以及优先确定症状严重者的疾病，这两个因素都影响死亡率的评估。我们将该模型应用于中国湖北省的数据，这是第一个受到SARS-CoV-2影响的地方，以及欧洲的六个地点-奥地利，巴伐利亚（德国），巴登-符腾堡（德国），伦巴第（意大利），西班牙和瑞士-以估计CFR，sCFR和IFR。校正偏倚后，sCFR和IFR的估计值彼此相似，且地理差异小于CFR。IFR最低的是瑞士（0.5%），最高的是湖北省（2.9%）。IFR随着年龄的增长而增加;在80岁或以上的人群中，估计值从瑞士的20%到西班牙的34%不等。这些发现意味着什么？CFR不能很好地预测SARS-CoV-2感染的总体死亡率，不应用于评估政策或进行地理位置之间的比较。SARS-CoV-2的IFR存在地理差异，这可能是由于应急准备和响应以及卫生服务能力等因素的差异。SARS-CoV-2感染导致大量死亡。背景截至2020年5月16日，全球已报告超过450万例严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）病例和超过30万例死亡病例。SARS-CoV-2感染死亡率的可靠估计对于了解临床预后、规划医疗能力和流行病预测至关重要。病死率（CFR）根据报告病例总数和报告死亡总数计算，是最常见的报告指标，但它可能是总体死亡率的误导性衡量标准。本研究的目的是（1）使用公开可用的监测数据模拟SARS-CoV-2的传播动力学，（2）推断调整偏倚后的SARS-CoV-2死亡率估计值，并检查不同地理位置的CFR、症状性病死率（sCFR）和感染病死率（IFR）。方法和结果我们开发了一个年龄分层的易感暴露感染去除（SEIR）房室模型描述的传播和死亡率的动态在SARS-CoV-2流行。我们的模型考虑了两个偏差：优先确定严重病例和死亡率的右删失。我们将传播模型与中国湖北省的监测数据进行了拟合，并将相同的模型应用于欧洲的六个地区：奥地利、巴伐利亚（德国）、巴登-符腾堡（德国）、伦巴第（意大利）、西班牙和瑞士。在湖北，基线估计值如下：CFR 2.4%（95%可信区间[CrI] 2.1%-2.8%），sCFR 3.7%（3.2%-4.2%）和IFR 2.9%（2.4%-3.5%）。死亡率的估计指标随时间而变化。在欧洲的六个地点，CFR的估计值差异很大。校正偏倚后的sCFR和IFR估计值彼此更为相似，但仍显示出一定程度的异质性。IFR的估计值范围从瑞士的0.5%（95% CrI 0.4%-0.6%）到意大利伦巴第的1.4%（1.1%-1.6%）。在所有地点，死亡率都随着年龄的增长而增加。在80岁或80岁以上的人中，IFR的估计表明，所有感染SARS-CoV-2的人死亡的比例从瑞士的20%（95%CrI 16%-26%）到西班牙的34%（95%CrI 28%-40%）不等。该模型的一个局限性是需要按发病日期分列的计数数据，但并非所有国家都有这些数据。结论我们提出了一个全面的解决方案，估计SARS冠状病毒2死亡率的监测数据在爆发期间。CFR不是SARS-CoV-2总体死亡率的良好预测指标，不应用于政策评估或跨环境比较。IFR的地理差异表明，单一的IFR不应适用于所有情况，以估计不同国家的SARS-CoV-2疫情的总规模。sCFR和IFR经过右删失和优先确定严重病例的调整，是可用于改善和监测临床和公共卫生策略以减少SARS-CoV-2感染死亡的措施。
Author summaryWhy was this study done? Reliable estimates of measures of mortality from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are needed to understand clinical prognosis, to plan healthcare capacity, and for epidemic forecasting. The case-fatality ratio (CFR), the number of reported deaths divided by the number of reported cases at a specific time point, is the most commonly used metric, but it is a biased measure of mortality from SARS-CoV-2 infection. The symptomatic case-fatality ratio (sCFR) and overall infection-fatality ratio (IFR) are alternative measures of mortality with clinical and public health relevance, which should be investigated further in different geographic locations. What did the researchers do and find? We developed a mathematical model that describes infection transmission and death during a SARS-CoV-2 epidemic. The model takes into account the delay between infection and death and preferential ascertainment of disease in people with severe symptoms, both of which affect the assessment of mortality. We applied the model to data from Hubei Province in China, which was the first place affected by SARS-CoV-2, and to six locations in Europe-Austria, Bavaria (Germany), Baden-Wurttemberg (Germany), Lombardy (Italy), Spain, and Switzerland-to estimate the CFR, the sCFR, and the IFR. Estimates of sCFR and IFR, adjusted for bias, were similar to each other and varied less geographically than the CFR. IFR was lowest in Switzerland (0.5%) and highest in Hubei Province (2.9%). The IFR increased with age; among those 80 years or older, estimates ranged from 20% in Switzerland to 34% in Spain. What do these findings mean? The CFR does not predict overall mortality from SARS-CoV-2 infection well and should not be used for the evaluation of policy or for making comparisons between geographic locations. There are geographic differences in the IFR of SARS-CoV-2, which could result from differences in factors including emergency preparedness and response and health service capacity. SARS-CoV-2 infection results in substantial mortality. Further studies should investigate ways to reduce death from SARS-CoV-2 in older people and to understand the causes of the differences between countries.Background As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case-fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case-fatality ratio (sCFR), and the infection-fatality ratio (IFR) in different geographic locations. Method and findings We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Wurttemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%-2.8%), sCFR 3.7% (3.2%-4.2%), and IFR 2.9% (2.4%-3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%-0.6%) in Switzerland to 1.4% (1.1%-1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%-26%) in Switzerland to 34% (95% CrI 28%-40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries. Conclusions We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.