Evidence map›Paper›PMID 37194011›Full record

ArticleBMC infectious diseases2023

Lessons drawn from Shanghai for controlling highly transmissible SARS-CoV-2 variants: insights from a modelling study.

Hao Wang, Tangjuan Li, Huan Gao, Chenxi Huang, Biao Tang, Sanyi Tang, Robert A Cheke, Weike Zhou

Abstract read
In one paragraph

Article in BMC infectious diseases, 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
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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

8 authors.

Hao WangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062, PR China.
Tangjuan LiSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
Huan GaoSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062, PR China.
Chenxi HuangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062, PR China.
Biao TangSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
Sanyi TangSchool of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062, PR China.
Robert A ChekeNatural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Kent, ME4 4TB, UK.
Weike ZhouSchool of Mathematics, Northwest University, Xi'an, 710127, PR China. wkzhou@snnu.edu.cn.

Funding

National Natural Science Foundation of China 12001349National Natural Science Foundation of China 12031010National Natural Science Foundation of China 12101488
6 · The paper itself

Abstract

backgroundThe continuous emergence of novel SARS-CoV-2 variants with markedly increased transmissibility presents major challenges to the zero-COVID policy in China. It is critical to adjust aspects of the policy about non-pharmaceutical interventions (NPIs) by searching for and implementing more effective ways. We use a mathematical model to mimic the epidemic pattern of the Omicron variant in Shanghai to quantitatively show the control challenges and investigate the feasibility of different control patterns in avoiding other epidemic waves.

methodsWe initially construct a dynamic model with a core step-by-step release strategy to reveal its role in controlling the spread of COVID-19, including the city-based pattern and the district-based pattern. We used the least squares method and real reported case data to fit the model for Shanghai and its 16 districts, respectively. Optimal control theory was utilized to explore the quantitative and optimal solutions of the time-varying control strength (i.e., contact rate) to suppress the highly transmissible SARS-CoV-2 variants.

resultsThe necessary period for reaching the zero-COVID goal can be nearly 4 months, and the final epidemic size was 629,625 (95%CI: [608,049, 651,201]). By adopting the city-based pattern, 7 out of 16 strategies released the NPIs more or earlier than the baseline and ensured a zero-resurgence risk at the average cost of 10 to 129 more cases in June. By adopting the district-based pattern, a regional linked release can allow resumption of social activity to ~ 100% in the boundary-region group about 14 days earlier and allow people to flow between different districts without causing infection resurgence. Optimal solutions of the contact rate were obtained with various testing intensities, and higher diagnosis rate correlated with higher optimal contact rate while the number of daily reported cases remained almost unchanged.

conclusionsShanghai could have been bolder and more flexible in unleashing social activity than they did. The boundary-region group should be relaxed earlier and more attention should be paid to the centre-region group. With a more intensive testing strategy, people could return to normal life as much as possible but still ensure the epidemic was maintained at a relatively low level.

Indexed as

COVID-19EpidemicsChinaHumansSARS-CoV-2City-based patternCOVID-19District-based patternOptimal controlStep-by-step NPI release strategy

Identifiers

PMID37194011
PMCPMC10186324

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.