Evidence map›Paper›PMID 37243113›Full record

ArticleVaccines2023

Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China.

Jiaqi Sun, Yusi Li, Lin-Fan Xiao, Ning-Yi Shao, Miao Liu

Abstract read
In one paragraph

Article in Vaccines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Jiaqi SunDepartment of Mathematics, Faculty of Science and Technology, University of Macau, Taipa, Macau, China.
Yusi LiDepartment of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macau, China.ORCID 0000-0003-3316-2770
Lin-Fan XiaoDepartment of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macau, China.
Ning-Yi ShaoDepartment of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macau, China.
Miao LiuDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

Funding

the Science and Technology Development Fund (FDCT) of Macau FDCT/0004/2021/AKP and FDCT/0038/2020/AFJthe University of Macau internal grant SRG2019-00177-FHS.
6 · The paper itself

Abstract

China is relaxing COVID-19 measures from the "dynamic zero tolerance" (DZT) level. The "flatten-the-curve" (FTC) strategy, which decreases and maintains the low rate of infection to avoid overwhelming the healthcare system by adopting relaxed nonpharmaceutical interventions (NPIs) after the outbreak, has been perceived as the most appropriate and effective method in preventing the spread of the Omicron variant. Hence, we established an improved data-driven model of Omicron transmission based on the age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model constructed by Cai to deduce the overall prevention effect throughout China. At the current level of immunity without the application of any NPIs, more than 1.27 billion (including asymptomatic individuals) were infected within 90 days. Moreover, the Omicron outbreak would result in 1.49 million deaths within 180 days. The application of FTC could decrease the number of deaths by 36.91% within 360 days. The strict implementation of FTC policy combined with completed vaccination and drug use, which only resulted in 0.19 million deaths in an age-stratified model, will help end the pandemic within about 240 days. The pandemic would be successfully controlled within a shorter period of time without a high fatality rate; therefore, the FTC policy could be strictly implemented through enhancement of immunity and drug use.

Indexed as

COVID-19data-driven modeling analysisflatten-the-curve policy

Identifiers

PMID37243113
PMCPMC10224169

What OpenQuestion holds

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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.