Evidence map›Paper›PMID 37953982›Full record

ArticleIEEE transactions on services computing2023

Multi-Criteria Performance Analysis Based on Physics of Decision - Application to COVID-19 and Future Pandemics.

Nafe Moradkhani, Frederick Benaben, Benoit Montreuil, Matthieu Lauras, Julien Jeany, Louis Faugre

Abstract read
In one paragraph

Article in IEEE transactions on services computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

6 authors.

Nafe MoradkhaniCentre Gnie Industriel, IMT Mines AlbiUniversity of Toulouse 81000 Albi France.ORCID https://orcid.org/0000-0001-9002-904X
Frederick BenabenCentre Gnie Industriel, IMT Mines AlbiUniversity of Toulouse 81000 Albi France.
Benoit MontreuilPhysical Internet Center, ISyEGeorgia Tech Atlanta GA 30332 USA.ORCID https://orcid.org/0000-0001-9909-9212
Matthieu LaurasCentre Gnie Industriel, IMT Mines AlbiUniversity of Toulouse 81000 Albi France.ORCID https://orcid.org/0000-0003-2901-4038
Julien JeanyScalian Lab 31000 Toulouse France.
Louis FaugrePhysical Internet Center, ISyEGeorgia Tech Atlanta GA 30332 USA.ORCID https://orcid.org/0000-0001-6835-6714

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this study is to present a novel perspective on decision support based on the conventional SEIR pandemic model paradigm considering the risks and opportunities as physical forces deviating the expected performance trajectory of a system. The impact of a pandemic is measured by the deviation of the social system's performance trajectory within the geometrical framework of its Key Performance Indicators (KPIs). According to the overall premise of utilizing Ordinary Differential Equations to simulate epidemics, the deviations are connected to several alternative interventions. The model is essentially built on two sets of parameters: (i) social system parameters and (ii) pandemic parameters. The ultimate objective is to propose a multi-criteria performance framework to control pandemics that includes a combination of timely measures. On the one hand, the current study optimizes prospective strategies to manage the potential future pandemic, while on the other hand, it explores the COVID-19 epidemic in the state of Georgia (USA).

Indexed as

Decision Support System (DSS)optimizationpandemicperformance managementrisk managementSEIR

Identifiers

PMID37953982
PMCPMC10620957

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.