Evidence map›Paper›PMID 30786869›Full record

ArticleBMC infectious diseases2019

A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan.

Jun Cai, Bing Zhang, Bo Xu, Karen Kie Yan Chan, Gerardo Chowell, Huaiyu Tian, Bing Xu

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 3 countries.

Jun CaiMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.ORCID http://orcid.org/0000-0001-9495-1226
Bing ZhangSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China.
Bo XuMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
Karen Kie Yan ChanMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
Gerardo ChowellSchool of Public Health, Georgia State University, Atlanta, GA, 30302, USA.
Huaiyu TianState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
Bing XuMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China. bingxu@tsinghua.edu.cn.
Ministry of Education · METsinghua University · CNBeijing Normal University · CNGeorgia State University · USSun Yat-sen University · CN

Funding

Beijing Natural Science Foundation JQ18025National Key Research and Development Program of China 2016YFA0600104National Natural Science Foundation of China 81673234Young Elite Scientist Sponsorship Program by CAST(YESS) 2018QNRC001
6 · The paper itself

Abstract

backgroundDetecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems.

methodsWe applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week.

resultsThe MCM and SRM yielded complete estimates for each of Japan's 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R

conclusionsThe Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.

Indexed as

Models, StatisticalSeasonsEpidemicsHumansInfluenza, HumanJapanLeast-Squares AnalysisLikelihood FunctionsRegression AnalysisSentinel SurveillanceStatistics as TopicEpidemic thresholdInfluenza surveillanceJapanMaximum curvature methodMCMNon-thresholding methodSegmented regression

Identifiers

PMID30786869
PMCPMC6383251
OpenAlexW2921054528

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.