Evidence map›Paper›PMID 36589974›Full record

ArticleFrontiers in public health2022

Modeling the resumption of work and production of enterprises during COVID-19: An SIR-based quantitative framework.

Hongchao Zhao, Zili Huang, Lei Xu, Junqing Tang, Yuang Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 49% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 4 institutions in 1 country.

Hongchao ZhaoDepartment of Trade Economics, Renmin Business School, Renmin University of China, Beijing, China.
Zili HuangSchool of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China.
Lei XuGeorgia Tech Shenzhen Institute, Tianjin University, Shenzhen, China.
Junqing TangSchool of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China.
Yuang ChenGeorgia Tech Shenzhen Institute, Tianjin University, Shenzhen, China.
Tianjin University · CNPeking University · CNRenmin University of China · CNShenzhen Research Institute of Big Data · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing COVID-19 pandemic has evolved beyond being a public health crisis as it has exerted worldwide severe economic impacts, triggering cascading failures in the global industrial network. Although certain powerful enterprises can remain its normal operation during this global shock, what's more likely to happen for the majority, especially those small- and medium-sized firms, is that they are experiencing temporary suspension out of epidemic control requirement, or even permanent closure due to chronic business losses. For those enterprises that sustain the pandemic and only suspend for a relatively short period, they could resume work and production when epidemic control and prevention conditions are satisfied and production and operation are adjusted correspondingly. In this paper, we develop a novel quantitative framework which is based on the classic susceptible-infectious-recovered (SIR) epidemiological model (i.e., the SIR model), containing a set of differential equations to capture such enterprises' reactions in response to COVID-19 over time. We fit our model from the resumption of work and production (RWP) data on industrial enterprises above the designated size (IEDS). By modeling the dynamics of enterprises' reactions, it is feasible to investigate the ratio of enterprises' state of operation at given time. Since enterprises are major economic entities and take responsibility for most output, this study could potentially help policy makers better understand the economic impact caused by the pandemic and could be heuristic for future prevention and resilience-building strategies against suchlike outbreaks of public health crises.

Indexed as

Communicable DiseasesCOVID-19Administrative PersonnelDisease OutbreaksHumansPandemicsCOVID-19 pandemiceconomic lossindustrial enterprisessimulationSIR model

Identifiers

PMID36589974
PMCPMC9801714
OpenAlexW4311635506

What OpenQuestion holds

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LicenceCC BY
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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.