Evidence map›Paper›PMID 37260525›Full record

ArticleFrontiers in digital health2023

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses.

Vedant Das Swain, Jiajia Xie, Maanit Madan, Sonia Sargolzaei, James Cai, Munmun De Choudhury, Gregory D Abowd, Lauren N Steimle, B Aditya Prakash

Open access · goldAbstract read
In one paragraph

Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
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

9 authors at 3 institutions in 1 country.

Vedant Das SwainCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Jiajia XieCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Maanit MadanCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Sonia SargolzaeiCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
James CaiDepartment of Computer Science, Brown University, Providence, RI, United States.
Munmun De ChoudhuryCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Gregory D AbowdCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Lauren N SteimleH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
B Aditya PrakashCollege of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Georgia Institute of Technology · USBrown University · USNortheastern University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as

Indexed as

COVID-19mobilitymodelingnon-pharmaceutical interventionpolicyWiFi

Identifiers

PMID37260525
PMCPMC10227502
OpenAlexW4376956237

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.