Evidence map›Paper›PMID 35935742›Full record

ArticleAnnals of operations research2022

Data analytics during pandemics: a transportation and location planning perspective.

Elif Bozkaya, Levent Eriskin, Mumtaz Karatas

Abstract read
In one paragraph

Article in Annals of operations research, 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
–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

0 citing papers in PubMed.

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

3 authors.

Elif BozkayaDepartment of Computer Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey.ORCID https://orcid.org/0000-0001-6960-2585
Levent EriskinDepartment of Industrial Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey.ORCID https://orcid.org/0000-0002-9128-2167
Mumtaz KaratasDepartment of Industrial Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey.ORCID 0000-0002-6287-3216

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Indexed as

COVID-19Data-driven analyticsLocationPandemicTransportation

Identifiers

PMID35935742
PMCPMC9342597

What OpenQuestion holds

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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.