Evidence map›Paper›PMID 33039365›Full record

ArticleMathematical biosciences2020

Four-tier response system and spatial propagation of COVID-19 in China by a network model.

Jing Ge, Daihai He, Zhigui Lin, Huaiping Zhu, Zian Zhuang

Abstract read
In one paragraph

Article in Mathematical biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 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

5 authors.

Jing GeSchool of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, China.
Daihai HeDepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
Zhigui LinSchool of Mathematical Science, Yangzhou University, Yangzhou, 225002, China. Electronic address: zglin@yzu.edu.cn.
Huaiping ZhuLamps and Centre for Disease Modelling (CDM), Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada.
Zian ZhuangDepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to investigate the effectiveness of lockdown and social distancing restrictions, which have been widely carried out as policy choice to curb the ongoing COVID-19 pandemic around the world, we formulate and discuss a staged and weighted network system based on a classical SEAIR epidemiological model. Five stages have been taken into consideration according to four-tier response to Public Health Crisis, which comes from the National Contingency Plan in China. Staggered basic reproduction number has been derived and we evaluate the effectiveness of lockdown and social distancing policies under different scenarios among 19 cities/regions in mainland China. Further, we estimate the infection risk associated with the sequential release based on population mobility between cities and the intensity of some non-pharmaceutical interventions. Our results reveal that Level I public health emergency response is necessary for high-risk cities, which can flatten the COVID-19 curve effectively and quickly. Moreover, properly designed staggered-release policies are extremely significant for the prevention and control of COVID-19, furthermore, beneficial to economic activities and social stability and development.

Indexed as

BetacoronavirusPandemicsBasic Reproduction NumberBiostatisticsChinaCitiesComputer SimulationCoronavirus InfectionsCOVID-19HumansModels, StatisticalPneumonia, ViralPublic HealthPublic PolicyQuarantineSARS-CoV-2Basic reproduction numberCOVID-19 pandemicFour-tier response systemGraph Laplacian operatorLockdownNetwork model

Identifiers

PMID33039365
PMCPMC7544595

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.