Evidence map›Paper›PMID 36828532›Full record

ArticleTropical medicine and infectious disease2023

Estimating Dengue Transmission Intensity in China Using Catalytic Models Based on Serological Data.

Ning Li, Haidong Li, Zhengji Chen, Huan Xiong, Zhibo Li, Tao Wei, Wei Liu, Xu-Sheng Zhang

Abstract read
In one paragraph

Article in Tropical medicine and infectious disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Dengue outbreaks in China during 2005-2023: a systematic analysis.Frontiers in cellular and infection microbiology · 2026
    Pooled it
  2. Article
  3. Article
  4. 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

8 authors.

Ning LiSchool of Public Health, Kunming Medical University, Kunming 650500, China.ORCID 0000-0003-2532-9251
Haidong LiSchool of Public Health, Kunming Medical University, Kunming 650500, China.
Zhengji ChenSchool of Public Health, Kunming Medical University, Kunming 650500, China.
Huan XiongSchool of Public Health, Kunming Medical University, Kunming 650500, China.
Zhibo LiSchool of Public Health, Kunming Medical University, Kunming 650500, China.ORCID 0000-0003-4582-1956
Tao WeiLibrary, Kunming Medical University, Kunming 650500, China.
Wei LiuSchool of Public Health, Kunming Medical University, Kunming 650500, China.
Xu-Sheng ZhangStatistics, Modelling and Economics Department, Data, Analytics & Surveillance, UK Health Security Agency, London NW9 5EQ, UK.ORCID 0000-0002-6596-4500

Funding

National Nature Science Foundation of China; Innovative Research Team of Yunnan Province, China. No.81860607; No.2019(6)
6 · The paper itself

Abstract

In recent decades, the global incidence of dengue has risen sharply, with more than 75% of infected people showing mild or no symptoms. Since the year 2000, dengue in China has spread quickly. At this stage, there is an urgent need to fully understand its transmission intensity and spread in China. Serological data provide reliable evidence for symptomatic and recessive infections. Through a literature search, we included 23 studies that collected age-specific serological dengue data released from 1980 to 2021 in China. Fitting four catalytic models to these data, we distinguished the transmission mechanisms by deviation information criterion and estimated force of infection and basic reproduction number (

Indexed as

Bayesian inferencecatalytic modeldenguemathematical modellingserological datatransmission intensity

Identifiers

PMID36828532
PMCPMC9967418

What OpenQuestion holds

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LicenceCC BY
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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.