Evidence map›Paper›PMID 35164963›Full record

ArticleISA transactions2022

Lessons drawn from China and South Korea for managing COVID-19 epidemic: Insights from a comparative modeling study.

Biao Tang, Fan Xia, Nicola Luigi Bragazzi, Zachary McCarthy, Xia Wang, Sha He, Xiaodan Sun, Sanyi Tang, Yanni Xiao, Jianhong Wu

Abstract read
In one paragraph

Article in ISA transactions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

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

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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

10 authors.

Biao TangThe Interdisciplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada, M3J 1P3.
Fan XiaThe Interdisciplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
Nicola Luigi BragazziLaboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada, M3J 1P3.
Zachary McCarthyLaboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada, M3J 1P3; Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Ontario, Canada, M3J 1P3.
Xia WangSchool of Mathematics and Information Science, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Sha HeSchool of Mathematics and Information Science, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Xiaodan SunThe Interdisciplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
Sanyi TangSchool of Mathematics and Information Science, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Yanni XiaoThe Interdisciplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
Jianhong WuThe Interdisciplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada, M3J 1P3; Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Ontario, Canada, M3J 1P3.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We conducted a comparative study of the COVID-19 epidemic in three different settings: mainland China, the Guangdong province of China and South Korea, by formulating two disease transmission dynamics models which incorporate epidemic characteristics and setting-specific interventions, and fitting the models to multi-source data to identify initial and effective reproduction numbers and evaluate effectiveness of interventions. We estimated the initial basic reproduction number for South Korea, the Guangdong province and mainland China as 2.6 (95% confidence interval (CI): (2.5, 2.7)), 3.0 (95%CI: (2.6, 3.3)) and 3.8 (95%CI: (3.5,4.2)), respectively, given a serial interval with mean of 5 days with standard deviation of 3 days. We found that the effective reproduction number for the Guangdong province and mainland China has fallen below the threshold 1 since February 8th and 18th respectively, while the effective reproduction number for South Korea remains high until March 2nd Moreover our model-based analysis shows that the COVID-19 epidemics in South Korean is almost under control with the cumulative confirmed cases tending to be stable as of April 14th. Through sensitivity analysis, we show that a coherent and integrated approach with stringent public health interventions is the key to the success of containing the epidemic in China and especially its provinces outside its epicenter. In comparison, we find that the extremely high detection rate is the key factor determining the success in controlling the COVID-19 epidemics in South Korea. The experience of outbreak control in mainland China and South Korea should be a guiding reference for the rest of the world.

Indexed as

COVID-19EpidemicsBasic Reproduction NumberChinaHumansSARS-CoV-2Comparative studyCOVID-19 epidemicMainland china and south koreaMathematical modelMulti-source data

Identifiers

PMID35164963
PMCPMC8713134

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.