Evidence map›Paper›PMID 38288541›Full record

ArticleJournal of Korean medical science2024

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

Seul-Ki Kang, Woo-Sik Son, Bryan Inho Kim

Open access · goldAbstract read
In one paragraph

Article in Journal of Korean medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Amelioration of the Influenza Epidemic Alert.Journal of Korean medical science · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Seul-Ki KangDivision of Infectious Disease Control, Bureau of Infectious Disease Policy, Korea Disease Control and Prevention Agency, Cheongju, Korea.ORCID https://orcid.org/0009-0003-0667-759X
Woo-Sik SonResearch Team for Transmission Dynamics of Infectious Disease, Division of Fundamental Research on Public Agenda, National Institute for Mathematical Sciences, Daejeon, Korea.ORCID https://orcid.org/0000-0002-0421-4546
Bryan Inho KimDivision of Infectious Disease Control, Bureau of Infectious Disease Policy, Korea Disease Control and Prevention Agency, Cheongju, Korea. globalepi@korea.kr.ORCID https://orcid.org/0000-0002-1798-5315
Korea Disease Control and Prevention Agency · KRNational Institute for Mathematical Sciences · KR

Funding

National Institute for Mathematical Sciences NIMS-B23730000
6 · The paper itself

Abstract

backgroundIn 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.

methodsThe 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.

resultsAs 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.

conclusionThe 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.

Indexed as

COVID-19Influenza A Virus, H1N1 SubtypeInfluenza, HumanVirus DiseasesHumansPandemicsSeasonsEarly Epidemic Alert SystemInfluenza Epidemic ThresholdInfluenza-Like Illness (ILI)Seasonal InfluenzaTime Derivative

Identifiers

PMID38288541
PMCPMC10825455
OpenAlexW4391006628

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.