Evidence map›Paper›PMID 33177237›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

Network interventions for managing the COVID-19 pandemic and sustaining economy.

Akihiro Nishi, George Dewey, Akira Endo, Sophia Neman, Sage K Iwamoto, Michael Y Ni, Yusuke Tsugawa, Georgios Iosifidis, Justin D Smith, Sean D Young

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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  18. A simple model for control of COVID-19 infections on an urban campus.Proceedings of the National Academy of Sciences of the United States of America · 2021
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Akihiro NishiDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095; akihironishi@ucla.edu.ORCID 0000-0003-1629-5985
George DeweyDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095.ORCID 0000-0001-5661-6494
Akira EndoDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom.ORCID 0000-0001-6377-7296
Sophia NemanSchool of Medicine, Medical College of Wisconsin, Wauwatosa, WI 53213.ORCID 0000-0002-8345-9321
Sage K IwamotoCollege of Letters & Science, University of California, Berkeley, CA 94720.ORCID 0000-0002-1870-5485
Michael Y NiSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 999077, Hong Kong Special Administrative Region, China.ORCID 0000-0002-1217-5858
Yusuke TsugawaDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA 90024.ORCID 0000-0002-1937-4833
Georgios IosifidisSchool of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland.ORCID 0000-0003-1001-2323
Justin D SmithDepartment of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT 84108.
Sean D YoungUniversity of California Institute for Prediction Technology, Department of Informatics, University of California, Irvine, CA 92617.ORCID 0000-0001-6052-4875

Funding

INNOVATIVE IMPLEMENTATION RESEARCH METHODS COREP30DA027828 · NIDA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI BROWN, C. HENDRICKS, MUSTANSKI, BRIAN · 2011 to 2020
$11.4M
NIDA NIH HHS P30 DA027828
6 · The paper itself

Abstract

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).

Indexed as

Social NetworkingComputer SimulationCOVID-19HumansPandemicsSystems Analysisagent-based simulationCOVID-19network interventionspandemic preparedness

Identifiers

PMID33177237
PMCPMC7720236

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.