Evidence map›Paper›PMID 35725257›Full record

ArticleBMJ open2022

Isolating the net effect of multiple government interventions with an extended Susceptible-Exposed-Infectious-Recovered (SEIR) framework: empirical evidence from the second wave of COVID-19 pandemic in China.

Jie Liu, Boya Gao, Helen Xiaohui Bao, Zhenwu Shi

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. 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
0.1field-weighted citation impact, top 61% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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 at 2 institutions in 2 countries.

Jie LiuSchool of Civil Engineering, Northeast Forestry University, Harbin, China.ORCID 0000-0002-7020-9702
Boya GaoSchool of Civil Engineering, Northeast Forestry University, Harbin, China.
Helen Xiaohui BaoDepartment of Land Economy, University of Cambridge, Cambridge, UK.
Zhenwu ShiSchool of Civil Engineering, Northeast Forestry University, Harbin, China shizhenwu@126.com.
Northeast Forestry University · CNUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveBy using a data-driven statistical approach, we isolated the net effect of multiple government interventions that were simultaneously implemented during the second wave of COVID-19 pandemic in China. DESIGN, DATA SOURCES AND ELIGIBILITY CRITERIA: We gathered epidemiological data and government interventions data of nine cities with local outbreaks during the second wave of COVID-19 pandemic in China. We employed the Susceptible-Exposed-Infectious-Recovered (SEIR) framework model to analyse the different pathways of transmission between cities with government interventions implementation and those without. We introduced new components to the standard SEIR model and investigated five themes of government interventions against COVID-19 pandemic. DATA EXTRACTION AND SYNTHESIS: We extracted information including study objective, design, methods, main findings and implications. These were tabulated and a narrative synthesis was undertaken given the diverse research designs, methods and implications.

resultsSupported by extensive empirical validation, our results indicated that the net effect of some specific government interventions (including masks, environmental cleaning and disinfection, tracing, tracking and 14-day centralised quarantining close contacts) had been significantly underestimated in the previous investigation. We also identified important moderators and mediators for the effect of certain government interventions, such as closure of shopping mall and restaurant in the medium-risk level areas, etc. Linking the COVID-19 epidemiological dynamics with the implementation timing of government interventions, we detected that the earlier implementation of some specific government interventions (including targeted partial lockdown, tracing, tracking and 14-day centralised quarantining close contacts) achieved the strongest and most timely effect on controlling COVID-19, especially at the early period of local outbreak.

conclusionsThese findings provide important scientific information for decisions regarding which and when government interventions should be implemented to fight against COVID-19 in China and beyond. The proposed analytical framework is useful for policy-making in future endemic and pandemic as well.

Indexed as

Communicable DiseasesCOVID-19ChinaCommunicable Disease ControlGovernmentHumansPandemicsSARS-CoV-2COVID-19epidemiologyhealth policypublic health

Identifiers

PMID35725257
PMCPMC9213777
OpenAlexW4283168988

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.