Evidence map›Paper›PMID 32760081›Full record

ArticlePloS one2020

Feasibility study of mitigation and suppression strategies for controlling COVID-19 outbreaks in London and Wuhan.

Po Yang, Jun Qi, Shuhao Zhang, Xulong Wang, Gaoshan Bi, Yun Yang, Bin Sheng, Geng Yang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.5field-weighted citation impact, top 12% of its field
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

18 citing papers in PubMed, 49 citations in OpenAlex.

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  17. ANN Assisted-IoT Enabled COVID-19 Patient Monitoring.IEEE access : practical innovations, open solutions · 2021
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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 at 4 institutions in 2 countries.

Po YangDepartment of Computer Science, The University of Sheffield, Sheffield, United Kingdom.ORCID 0000-0002-8553-7127
Jun QiDepartment of Engineering Science, University of Oxford, Oxford, United Kingdom.
Shuhao ZhangSchool of Software, Yunnan University, Yunnan, China.
Xulong WangSchool of Software, Yunnan University, Yunnan, China.
Gaoshan BiSchool of Software, Yunnan University, Yunnan, China.
Yun YangSchool of Software, Yunnan University, Yunnan, China.
Bin ShengDepartment of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, China.
Geng YangThe State Key Laboratory of Fluid Power and Electronic Systems, School of Mechanical Engineering, Zhejiang University, China.
Yunnan University · CNZhejiang University · CNShanghai Jiao Tong University · CNUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent outbreaks of coronavirus disease 2019 (COVID-19) has led a global pandemic cross the world. Most countries took two main interventions: suppression like immediate lockdown cities at epicenter or mitigation that slows down but not stopping epidemic for reducing peak healthcare demand. Both strategies have their apparent merits and limitations; it becomes extremely hard to conduct one intervention as the most feasible way to all countries. Targeting at this problem, this paper conducted a feasibility study by defining a mathematical model named SEMCR, it extended traditional SEIR (Susceptible-Exposed-Infectious-Recovered) model by adding two key features: a direct connection between Exposed and Recovered populations, and separating infections into mild and critical cases. It defined parameters to classify two stages of COVID-19 control: active contain by isolation of cases and contacts, passive contain by suppression or mitigation. The model was fitted and evaluated with public dataset containing daily number of confirmed active cases including Wuhan and London during January 2020 and March 2020. The simulated results showed that 1) Immediate suppression taken in Wuhan significantly reduced the total exposed and infectious populations, but it has to be consistently maintained at least 90 days (by the middle of April 2020). Without taking this intervention, we predict the number of infections would have been 73 folders higher by the middle of April 2020. Its success requires efficient government initiatives and effective collaborative governance for mobilizing of corporate resources to provide essential goods. This mode may be not suitable to other countries without efficient collaborative governance and sufficient health resources. 2) In London, it is possible to take a hybrid intervention of suppression and mitigation for every 2 or 3 weeks over a longer period to balance the total infections and economic loss. While the total infectious populations in this scenario would be possibly 2 times than the one taking suppression, economic loss and recovery of London would be less affected. 3) Both in Wuhan and London cases, one important issue of fitting practical data was that there were a portion (probably 62.9% in Wuhan) of self-recovered populations that were asymptomatic or mild symptomatic. This finding has been recently confirmed by other studies that the seroprevalence in Wuhan varied between 3.2% and 3.8% in different sub-regions. It highlights that the epidemic is far from coming to an end by means of herd immunity. Early release of intervention intensity potentially increased a risk of the second outbreak.

Indexed as

Asymptomatic DiseasesBetacoronavirusChinaCoronavirus InfectionsCOVID-19Disease OutbreaksFeasibility StudiesHumansLondonModels, TheoreticalPandemicsPneumonia, ViralSARS-CoV-2

Identifiers

PMID32760081
PMCPMC7410247
OpenAlexW3048106528

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.