Evidence map›Paper›PMID 35662902›Full record

ArticleInfectious Disease Modelling2022

Shigellosis seasonality and transmission characteristics in different areas of China: A modelling study.

Zeyu Zhao, Meng Yang, Jinlong Lv, Qingqing Hu, Qiuping Chen, Zhao Lei, Mingzhai Wang, Hao Zhang, Xiongjie Zhai, Benhua Zhao and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious Disease Modelling, 2022. 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
0.6field-weighted citation impact, top 34% 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, 7 citations in OpenAlex.

  1. Temporal trends ofFrontiers in public health · 2026
    Observational
  2. Genomic and epidemiological characteristics ofPathogens and global health · 2025
    Article
  3. Article
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

16 authors at 8 institutions in 4 countries.

Zeyu ZhaoState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China.
Meng YangState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China.
Jinlong LvDepartment of Mathematics, School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 102616, People's Republic of China.
Qingqing HuDivision of Public Health, School of Medicine, University of Utah, 201 Presidents Circle, Salt Lake City, 84112, Utah, USA.
Qiuping ChenCIRAD, UMR 17, Intertryp, Montpellier, France.
Zhao LeiState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China.
Mingzhai WangXiamen Center for Disease Control and Prevention, Xiamen City, Fujian Province, People's Republic of China.
Hao ZhangYichang Center for Disease Control and Prevention, Yichang City, Hubei Province, People's Republic of China.
Xiongjie ZhaiLongde County Center for Disease Control and Prevention, Guyuan City, Ningxia Hui Autonomous Region, People's Republic of China.
Benhua ZhaoState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China.
Yanhua SuState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China.
Yong ChenDepartment of Stomatology, School of Medicine, Xiamen University People's Republic of China.
Xu-Sheng ZhangPublic Health England, London, United Kingdom.
Jing-An CuiDepartment of Mathematics, School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 102616, People's Republic of China.
Roger FrutosCIRAD, UMR 17, Intertryp, Montpellier, France.
Tianmu ChenState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China.
Xiamen University · CNCentre de Coopération Internationale en Recherche Agronomique pour le Développement · FRBeijing University of Civil Engineering and Architecture · CNNingxia Center for Diseases Prevention and Control · CNPublic Health England · GBSecond Hospital of Yichang · CNUniversity of Utah · USXian Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In China, the burden of shigellosis is unevenly distributed, notably across various ages and geographical areas. Shigellosis temporal trends appear to be seasonal. We should clarify seasonal warnings and regional transmission patterns. Method: This study adopted a Logistic model to assess the seasonality and a dynamics model to compare the transmission in different areas. The next-generation matrix was used to calculate the effective reproduction number ( Results: In China, the rate of shigellosis fell from 35.12 cases per 100,000 people in 2005 to 7.85 cases per 100,000 people in 2017, peaking in June and August. After simulation by the Logistic model, the 'peak time' is mainly concentrated from mid-June to mid-July. China's 'early warning time' is primarily focused on from April to May. We predict the 'peak time' of shigellosis is the 6.30th month and the 'early warning time' is 3.87th month in 2021. According to the dynamics model results, the water/food transfer pathway has been mostly blocked off. The transmissibility of different regions varies greatly, such as the mean Conclusion: The 'early warning time' for shigellosis in China is from April to May every year, and it may continue to advance in the future, such as the early warning time in 2021 is in mid-March. Furthermore, we should focus on preventing and controlling the person-to-person route of shigellosis and stratified deploy prevention and control measures according to the regional transmission.

Indexed as

ARIMA, Autoregressive Integrated Moving Average (model)CDC, Center of Chinese Center for Disease Control and PreventionCI, confidence intervalEarly warningMSM, men who sex with a manODE, ordinary differential equationR0, basic reproductive numberR2, Coefficient of determinationReff, effective reproduction numberSD, standard deviationSeasonalitySEIAR, Susceptible–Exposed–Infectious/Asymptomatic–Recovered (model)SEIARW, Susceptible–Exposed–Infectious/Asymptomatic–Recovered-Water/Food (model)ShigellosisTransmissibility

Identifiers

PMID35662902
PMCPMC9144056
OpenAlexW4281396308

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.