Evidence map›Paper›PMID 34258483›Full record

ArticleInfectious Disease Modelling2021

Impact of school reopening on pandemic spread: A case study using an agent-based model for COVID-19.

Hanisha Tatapudi, Tapas K Das

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  3. Article
  4. Article
  5. Article
  6. 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

2 authors.

Hanisha TatapudiDepartment of Industrial and Management System Engineering, University of South Florida, Tampa, FL, USA.
Tapas K DasDepartment of Industrial and Management System Engineering, University of South Florida, Tampa, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article examines the impact of partial/full reopening of school/college campuses on the spread of a pandemic using COVID-19 as a case study. The study uses an agent-based simulation model that replicates community spread in an urban region of U.S.A. via daily social mixing of susceptible and infected individuals. Data representing population demographics, SARS-CoV-2 epidemiology, and social interventions guides the model's behavior, which is calibrated and validated using data reported by the government. The model indicates a modest but significant increase (8.15%) in the total number of reported cases in the region for a complete (100%) reopening compared to keeping schools and colleges fully virtual. For partial returns of 75% and 50%, the percent increases in the number of reported cases are shown to be small (2.87% and 1.26%, respectively) and statistically insignificant. The AB model also predicts that relaxing the stringency of the school safety protocol for sanitizing, use of mask, social distancing, testing, and quarantining and thus allowing the school transmission coefficient to double may result in a small increase in the number of reported infected cases (2.14%). Hence for pandemic outbreaks from viruses with similar characteristics as for SARS-CoV-2, keeping the schools and colleges open with a modest campus safety protocol and in-person attendance below a certain threshold may be advisable.

Indexed as

Agent-based simulation modelAge-specific impactCOVID-19SARS-CoV-2School reopeningSchool transmission rate

Identifiers

PMID34258483
PMCPMC8265176

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.