Evidence map›Paper›PMID 35813002›Full record

ArticleIEEE access : practical innovations, open solutions2021

DST-Predict: Predicting Individual Mobility Patterns From Mobile Phone GPS Data.

Syed Mohammed Arshad Zaidi, Varun Chandola, Eun-Hye Yoo

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

Article in IEEE access : practical innovations, open solutions, 2021. 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 27% 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

3 authors at 1 institution in 1 country.

Syed Mohammed Arshad ZaidiComputer Science and Engineering, University at Buffalo-SUNY, Buffalo, NY 14260, USA.ORCID 0000-0002-7579-8892
Varun ChandolaComputer Science and Engineering, University at Buffalo-SUNY, Buffalo, NY 14260, USA.
Eun-Hye YooDepartment of Geography, University at Buffalo-SUNY, Buffalo, NY 14260, USA.ORCID 0000-0001-5057-2187
University at Buffalo, State University of New York · US

Funding

A three-population three-scale social network model to assess disease dispersionR01GM108731 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BIAN, LING, CHEN, CYNTHIA · 2015 to 2019
$2.7M
NIGMS NIH HHS R01 GM108731
6 · The paper itself

Abstract

Predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) is important to improve our understanding of the complexity of human mobility patterns, and to capture anomalous behaviors in an individual's spatial movements, which can be particularly useful in situations such as those induced by the COVID-19 pandemic. We propose a system called

Indexed as

deep learningHuman mobilitypredictive learning

Identifiers

PMID35813002
PMCPMC9264728
OpenAlexW4206234171

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.