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及时的疫情监测:考虑到报告延迟的影响,预测纽约市 2020 年 9 月的 COVID-19 疫情高峰。

Timely epidemic monitoring in the presence of reporting delays: anticipating the COVID-19 surge in New York City, September 2020.

机构信息

Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Eisner Health, Los Angeles, CA, 90015, USA.

出版信息

BMC Public Health. 2022 May 2;22(1):871. doi: 10.1186/s12889-022-13286-7.

DOI:10.1186/s12889-022-13286-7
PMID:35501734
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9058738/
Abstract

BACKGROUND

During a fast-moving epidemic, timely monitoring of case counts and other key indicators of disease spread is critical to an effective public policy response.

METHODS

We describe a nonparametric statistical method, originally applied to the reporting of AIDS cases in the 1980s, to estimate the distribution of reporting delays of confirmed COVID-19 cases in New York City during the late summer and early fall of 2020.

RESULTS

During August 15-September 26, the estimated mean delay in reporting was 3.3 days, with 87% of cases reported by 5 days from diagnosis. Relying upon the estimated reporting-delay distribution, we projected COVID-19 incidence during the most recent 3 weeks as if each case had instead been reported on the same day that the underlying diagnostic test had been performed. Applying our delay-corrected estimates to case counts reported as of September 26, we projected a surge in new diagnoses that had already occurred but had yet to be reported. Our projections were consistent with counts of confirmed cases subsequently reported by November 7.

CONCLUSION

The projected estimate of recently diagnosed cases could have had an impact on timely policy decisions to tighten social distancing measures. While the recent advent of widespread rapid antigen testing has changed the diagnostic testing landscape considerably, delays in public reporting of SARS-CoV-2 case counts remain an important barrier to effective public health policy.

摘要

背景

在疫情迅速蔓延期间,及时监测病例数量和疾病传播的其他关键指标对于有效的公共政策应对至关重要。

方法

我们描述了一种非参数统计方法,该方法最初应用于 20 世纪 80 年代艾滋病病例的报告,以估计 2020 年夏末和初秋期间纽约市确诊 COVID-19 病例报告延迟的分布情况。

结果

在 8 月 15 日至 9 月 26 日期间,报告的平均延迟估计为 3.3 天,有 87%的病例在诊断后 5 天内报告。根据报告延迟分布的估计,我们预测了最近 3 周内 COVID-19 的发病率,假设每个病例都是在进行基础诊断测试的当天报告的。将我们延迟校正的估计数应用于截至 9 月 26 日报告的病例数,我们预测了已经发生但尚未报告的新诊断病例的激增。我们的预测与随后截至 11 月 7 日报告的确诊病例数相符。

结论

最近诊断病例的预测估计数可能对及时收紧社会距离措施的决策产生了影响。虽然最近广泛使用快速抗原检测已经极大地改变了诊断检测的格局,但 SARS-CoV-2 病例数的公共报告延迟仍然是有效公共卫生政策的一个重要障碍。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/149787e7e372/12889_2022_13286_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/661ac08c0b26/12889_2022_13286_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/7b9c425b47e0/12889_2022_13286_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/a534cca95f81/12889_2022_13286_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/149787e7e372/12889_2022_13286_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/661ac08c0b26/12889_2022_13286_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/7b9c425b47e0/12889_2022_13286_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/a534cca95f81/12889_2022_13286_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a482/9059425/149787e7e372/12889_2022_13286_Fig4_HTML.jpg

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