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时间导数(TD)方法在流感疫情早期预警中的应用。

Application of the Time Derivative (TD) Method for Early Alert of Influenza Epidemics.

机构信息

Division of Infectious Disease Control, Bureau of Infectious Disease Policy, Korea Disease Control and Prevention Agency, Cheongju, Korea.

Research Team for Transmission Dynamics of Infectious Disease, Division of Fundamental Research on Public Agenda, National Institute for Mathematical Sciences, Daejeon, Korea.

出版信息

J Korean Med Sci. 2024 Jan 29;39(4):e40. doi: 10.3346/jkms.2024.39.e40.

DOI:10.3346/jkms.2024.39.e40
PMID:38288541
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10825455/
Abstract

BACKGROUND

In order to minimize the spread of seasonal influenza epidemic to communities worldwide, the Korea Disease Control and Prevention Agency has issued an influenza epidemic alert using the influenza epidemic threshold formula based on the results of the influenza-like illness (ILI) rate. However, unusual changes have occurred in the pattern of respiratory infectious diseases, including seasonal influenza, after the coronavirus disease 2019 (COVID-19) pandemic. As a result, the importance of detecting the onset of an epidemic earlier than the existing epidemic alert system is increasing. Accordingly, in this study, the Time Derivative (TD) method was suggested as a supplementary approach to the existing influenza alert system for the early detection of seasonal influenza epidemics.

METHODS

The usefulness of the TD method as an early epidemic alert system was evaluated by applying the ILI rate for each week during past seasons when seasonal influenza epidemics occurred, ranging from the 2013-2014 season to the 2022-2023 season to compare it with the issued time of the actual influenza epidemic alert.

RESULTS

As a result of applying the TD method, except for the two seasons (2020-2021 season and 2021-2022 season) that had no influenza epidemic, an influenza early epidemic alert was suggested during the remaining seasons, excluding the 2017-2018 and 2022-2023 seasons.

CONCLUSION

The TD method is a time series analysis that enables early epidemic alert in real-time without relying on past epidemic information. It can be considered as an alternative approach when it is challenging to set an epidemic threshold based on past period information. This situation may arise when there has been a change in the typical seasonal epidemic pattern of various respiratory viruses, including influenza, following the COVID-19 pandemic.

摘要

背景

为了最大限度地减少季节性流感在全球社区的传播,韩国疾病控制与预防机构根据流感样疾病(ILI)率的结果,使用基于流感流行阈值公式发布了流感流行警报。然而,自 2019 年冠状病毒病(COVID-19)大流行以来,包括季节性流感在内的呼吸道传染病模式发生了异常变化。因此,比现有流行警报系统更早地检测到疫情的发生变得越来越重要。因此,在这项研究中,建议使用时间导数(TD)方法作为现有流感警报系统的补充方法,以便更早地发现季节性流感疫情。

方法

通过应用过去发生季节性流感疫情的每个季节每周的 ILI 率,评估 TD 方法作为早期流行警报系统的有用性,范围从 2013-2014 季节到 2022-2023 季节,并将其与实际流感流行警报的发布时间进行比较。

结果

除了两个没有流感疫情的季节(2020-2021 季节和 2021-2022 季节)之外,应用 TD 方法提示了其余季节的流感早期流行警报,除了 2017-2018 季节和 2022-2023 季节。

结论

TD 方法是一种时间序列分析,可以实时进行早期流行警报,而无需依赖过去的流行信息。当基于过去时间段的信息设置流行阈值具有挑战性时,可以将其视为一种替代方法。这种情况可能会在 COVID-19 大流行之后,各种呼吸道病毒(包括流感)的典型季节性流行模式发生变化时出现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d3b/10825455/468e699436b3/jkms-39-e40-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d3b/10825455/fac491f2ed76/jkms-39-e40-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d3b/10825455/ddd7f2dc0ea1/jkms-39-e40-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d3b/10825455/468e699436b3/jkms-39-e40-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d3b/10825455/fac491f2ed76/jkms-39-e40-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d3b/10825455/ddd7f2dc0ea1/jkms-39-e40-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d3b/10825455/468e699436b3/jkms-39-e40-g003.jpg

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本文引用的文献

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Early detection of influenza outbreak using time derivative of incidence.利用发病率的时间导数早期检测流感疫情。
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The Impact of Social Distancing on the Transmission of Influenza Virus, South Korea, 2020.2020年韩国社交距离措施对流感病毒传播的影响
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