Evidence map›Paper›PMID 32288597›Full record

ArticleTransportation research. Part E, Logistics and transportation review2020

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case.

Dmitry Ivanov

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Transportation research. Part E, Logistics and transportation review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 292 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
292citing papers in PubMed, 4 pooled it
357.6field-weighted citation impact, top 1% 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

292 citing papers in PubMed, 4 syntheses or guidelines pooled it, 2,121 citations in OpenAlex.

  1. Pooled it
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  3. Modeling COVID-19 Impacts and Response Strategies in the Construction Industry: PLS-SEM Approach.International journal of environmental research and public health · 2022
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  8. How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025
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232 more citing papers are in PubMed but not listed here.

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

1 author at 1 institution in 1 country.

Dmitry IvanovBerlin School of Economics and Law, Department of Business Administration, Professor for Supply Chain and Operations Management, 10825 Berlin, Germany.
Berlin School of Economics and Law · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemic outbreaks are a special case of supply chain (SC) risks which is distinctively characterized by a long-term disruption existence, disruption propagations (i.e., the ripple effect), and high uncertainty. We present the results of a simulation study that opens some new research tensions on the impact of COVID-19 (SARS-CoV-2) on the global SCs. First, we articulate the specific features that frame epidemic outbreaks as a unique type of SC disruption risks. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of epidemic outbreaks on the SC performance using the example of coronavirus COVID-19 and anyLogistix simulation and optimization software. We offer an analysis for observing and predicting both short-term and long-term impacts of epidemic outbreaks on the SCs along with managerial insights. A set of sensitivity experiments for different scenarios allows illustrating the model's behavior and its value for decision-makers. The major observation from the simulation experiments is that the timing of the closing and opening of the facilities at different echelons might become a major factor that determines the epidemic outbreak impact on the SC performance rather than an upstream disruption duration or the speed of epidemic propagation. Other important factors are lead-time, speed of epidemic propagation, and the upstream and downstream disruption durations in the SC. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of epidemic outbreaks on the SCs and develop pandemic SC plans. Our approach can also help to identify the successful and wrong elements of risk mitigation/preparedness and recovery policies in case of epidemic outbreaks. The paper is concluded by summarizing the most important insights and outlining future research agenda.

Indexed as

CoronavirusCOVID-19Digital twinEpidemic outbreakPandemic planResilienceRisk managementSARS-CoV-2SimulationSupply chain

Identifiers

PMID32288597
PMCPMC7147532
OpenAlexW3013474995

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.