Evidence map›Paper›PMID 36182803›Full record

ArticleEpidemics2022

Assessing K-12 school reopenings under different COVID-19 Spread scenarios - United States, school year 2020/21: A retrospective modeling study.

Timothy C Germann, Manhong Z Smith, Lori R Dauelsberg, Geoffrey Fairchild, Terece L Turton, Morgan E Gorris, Chrysm Watson Ross, James P Ahrens, Daniel D Hemphill, Carrie A Manore and 1 more

Abstract read
In one paragraph

Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Measures implemented in the school setting to contain the COVID-19 pandemicThe Cochrane database of systematic reviews · 2022
    Pooled it
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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

11 authors.

Timothy C GermannPhysics & Chemistry of Materials, Los Alamos National Laboratory, USA.
Manhong Z SmithInformation Systems & Modeling, Los Alamos National Laboratory, USA; Center for Nonlinear Studies, Los Alamos National Laboratory, USA.
Lori R DauelsbergInformation Systems & Modeling, Los Alamos National Laboratory, USA.
Geoffrey FairchildInformation Systems & Modeling, Los Alamos National Laboratory, USA.
Terece L TurtonInformation Sciences, Los Alamos National Laboratory, USA.
Morgan E GorrisInformation Systems & Modeling, Los Alamos National Laboratory, USA; Center for Nonlinear Studies, Los Alamos National Laboratory, USA.
Chrysm Watson RossInformation Systems & Modeling, Los Alamos National Laboratory, USA; Computer Science Department, University of New Mexico, USA.
James P AhrensNational Security Education Center, Los Alamos National Laboratory, USA.
Daniel D HemphillAdvanced Research in Cyber Systems, Los Alamos National Laboratory, USA.
Carrie A ManoreInformation Systems & Modeling, Los Alamos National Laboratory, USA.
Sara Y Del ValleInformation Systems & Modeling, Los Alamos National Laboratory, USA. Electronic address: sdelvall@lanl.gov.

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
CDC HHSNIGMS NIH HHS R01 GM130668
6 · The paper itself

Abstract

introductionSchool-age children play a key role in the spread of airborne viruses like influenza due to the prolonged and close contacts they have in school settings. As a result, school closures and other non-pharmaceutical interventions were recommended as the first line of defense in response to the novel coronavirus pandemic (COVID-19).

methodsWe used an agent-based model that simulates communities across the United States including daycares, primary, and secondary schools to quantify the relative health outcomes of reopening schools for the period of August 15, 2020 to April 11, 2021. Our simulation was carried out in early September 2020 and was based on the latest (at the time) Centers for Disease Control and Prevention (CDC)'s Pandemic Planning Scenarios released in May 2020. We explored different reopening scenarios including virtual learning, in-person school, and several hybrid options that stratify the student population into cohorts in order to reduce exposure and pathogen spread.

resultsScenarios where cohorts of students return to school in non-overlapping formats, which we refer to as hybrid scenarios, resulted in significant decreases in the percentage of symptomatic individuals with COVID-19, by as much as 75%. These hybrid scenarios have only slightly more negative health impacts of COVID-19 compared to implementing a 100% virtual learning scenario. Hybrid scenarios can significantly avert the number of COVID-19 cases at the national scale-approximately between 28 M and 60 M depending on the scenario-over the simulated eight-month period. We found the results of our simulations to be highly dependent on the number of workplaces assumed to be open for in-person business, as well as the initial level of COVID-19 incidence within the simulated community.

conclusionIn an evolving pandemic, while a large proportion of people remain susceptible, reducing the number of students attending school leads to better health outcomes; part-time in-classroom education substantially reduces health risks.

Indexed as

COVID-19ChildHumansPandemicsRetrospective StudiesSARS-CoV-2SchoolsUnited StatesAgent-based modelCOVID-19K-12 school reopening scenariosNon-pharmaceutical interventionsWorkplace restrictions

Identifiers

PMID36182803
PMCPMC9490957

What OpenQuestion holds

Textmetadata
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.