Evidence map›Paper›PMID 37650063›Full record

ArticleRoyal Society open science2023

Prediction of the next major outbreak of COVID-19 in Mainland China and a vaccination strategy for it.

Yuanyuan Wu, Weike Zhou, Sanyi Tang, Robert A Cheke, Xia Wang

Abstract read
In one paragraph

Article in Royal Society open science, 2023. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Observational
  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

5 authors.

Yuanyuan WuSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, People's Republic of China.ORCID 0009-0000-3755-1488
Weike ZhouSchool of Mathematics, Northwest University, Xi'an 710127, People's Republic of China.
Sanyi TangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, People's Republic of China.ORCID 0000-0002-3324-746X
Robert A ChekeNatural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Kent ME4 4TB, UK.ORCID 0000-0002-7437-1934
Xia WangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, People's Republic of China.ORCID 0000-0001-9317-4824

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After the widespread prevalence of COVID-19 at the end of 2022 in Mainland China, a major concern is when will the second major outbreak occur and with what prevalence and fatality rates will it be associated with, as peoples' immunity from natural infection subsides. To address this, we established an age-structured model considering vaccine and infection-derived immunity, fitted an immunity-waning curve, and calibrated the model using the epidemic and vaccination data from Hong Kong in 2022. The model and the situation of the first major epidemic in Mainland China were then used to predict the prevalence rate, fatality rate and peak time of the second wave. In addition, the controlling effects of different vaccination strategies on the second major outbreak are discussed. Finally, a characterization indicator for the level of population immunity was provided. We conclude that if the prevalence of the first major epidemic was 80%, the prevalence rate of the second major outbreak would be about 37.64%, and the peak time would have been July 2 2023. Strengthening vaccination can effectively delay the peak of the second wave of the epidemic and reduce the prevalence.

Indexed as

age-structured modelCOVID-19immunity waningvaccination strategy

Identifiers

PMID37650063
PMCPMC10465198

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.