Evidence map›Paper›PMID 36438437›Full record

ArticleIntelligent medicine2023

A model simulation on the SARS-CoV-2 Omicron variant containment in Beijing, China.

Shihao Liang, Tianhong Jiang, Zengtao Jiao, Zhengyuan Zhou

Abstract read
In one paragraph

Article in Intelligent medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

1 citing paper in PubMed.

  1. 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

4 authors.

Shihao LiangYidu Cloud Technology Inc, Beijing 100089, China.
Tianhong JiangChangshu Center for Disease Control and Prevention, Jiangsu 215501, China.
Zengtao JiaoYidu Cloud Technology Inc, Beijing 100089, China.
Zhengyuan ZhouChangshu Center for Disease Control and Prevention, Jiangsu 215501, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The Omicron variant of SARS-COV-2 is replacing previously circulating variants around the world in 2022. Sporadic outbreaks of the Omicron variant into China have posed a concern how to properly response to battle against evolving coronavirus disease 2019 (COVID-19). Methods: Based on the epidemic data from website announced by Beijing Center for Disease Control and Prevention for the recent outbreak in Beijing from April 22nd to June 8th in 2022, we developed a modified SEPIR model to mathematically simulate the customized dynamic COVID-zero strategy and project transmissions of the Omicron epidemic. To demonstrate the effectiveness of dynamic-changing policies deployment during this outbreak control, we modified the transmission rate into four parts according to policy-changing dates as April 22nd to May 2nd, May 3rd to 11st, May 12th to 21st, May 22nd to June 8th, and we adopted Markov chain Monte Carlo (MCMC) to estimate different transmission rate. Then we altered the timing and scaling of these measures used to understand the effectiveness of these policies on the Omicron variant. Results: The estimated effective reproduction number of four parts were 1.75 (95% CI 1.66-1.85), 0.89 (95% CI 0.79-0.99), 1.15 (95% CI 1.05-1.26) and 0.53 (95% CI 0.48 -0.60), respectively.  In the experiment, we found that till June 8th the cumulative cases would rise to 132,609 (95% CI 59,667-250,639), 73.39 times of observed cumulative cases number 1,807 if no policy were implemented on May 3rd, and would be 3,235 (95% CI 1,909 - 4,954), increased by 79.03% if no policy were implemented on May 22nd. A 3-day delay of the implementation of policies would led to increase of cumulative cases by 58.28% and a 7-day delay would led to increase of cumulative cases by 187.00%. On the other hand, taking control measures 3 or 7 days in advance would result in merely 38.63% or 68.62% reduction of real cumulative cases. And if lockdown implemented 3 days before May 3rd, the cumulative cases would be 289 (95% CI 211-378), reduced by 84%, and the cumulative cases would be 853 (95% CI 578-1,183), reduced by 52.79% if lockdown implemented 3 days after May 3rd. Conclusion: The dynamic COVID-zero strategy might be able to effectively minimize the scale of the transmission, shorten the epidemic period and reduce the total number of infections.

Indexed as

Compartmental modelsCOVID-19Dynamic COVID-zero strategyEpidemic modelingPrevention measures

Identifiers

PMID36438437
PMCPMC9677562

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.