Evidence map›Paper›PMID 36033592›Full record

ArticlePatterns (New York, N.Y.)2022

Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling study.

Ivan Specht, Kian Sani, Bryn C Loftness, Curtis Hoffman, Gabrielle Gionet, Amy Bronson, John Marshall, Craig Decker, Landen Bailey, Tomi Siyanbade and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

16 authors.

Ivan SpechtThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Kian SaniThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Bryn C LoftnessThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Curtis HoffmanThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Gabrielle GionetThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Amy BronsonColorado Mesa University, Grand Junction, CO 81501, USA.
John MarshallColorado Mesa University, Grand Junction, CO 81501, USA.
Craig DeckerDepartment of Microbiology and Molecular Biology, College of Life Sciences, Brigham Young University, Provo, UT 84606, USA.
Landen BaileyDepartment of Microbiology and Molecular Biology, College of Life Sciences, Brigham Young University, Provo, UT 84606, USA.
Tomi SiyanbadeThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Molly KemballThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Brett E PickettDepartment of Microbiology and Molecular Biology, College of Life Sciences, Brigham Young University, Provo, UT 84606, USA.
William P HanageThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Todd BrownThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Pardis C SabetiThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Andrés ColubriThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.

Indexed as

Bluetooth contact sensingepidemiologymodelingnetwork analysisoutbreak scienceoutbreak simulationpandemic mitigation

Identifiers

PMID36033592
PMCPMC9403368

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.