Evidence map›Paper›PMID 34237062›Full record

ArticlePLoS computational biology2021

COVID-19 modeling and non-pharmaceutical interventions in an outpatient dialysis unit.

Hankyu Jang, Philip M Polgreen, Alberto M Segre, Sriram V Pemmaraju

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. 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
0.5field-weighted citation impact, top 34% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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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

4 authors at 1 institution in 1 country.

Hankyu JangDepartment of Computer Science, The University of Iowa, Iowa City, Iowa, United States of America.ORCID 0000-0002-3056-4347
Philip M PolgreenDepartment of Internal Medicine, Division of Infectious Diseases, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, United States of America.ORCID 0000-0002-0630-5898
Alberto M SegreDepartment of Computer Science, The University of Iowa, Iowa City, Iowa, United States of America.
Sriram V PemmarajuDepartment of Computer Science, The University of Iowa, Iowa City, Iowa, United States of America.ORCID 0000-0002-0834-3476
University of Iowa · US

Funding

ACL HHS U01CK000531NCEZID CDC HHS U01 CK000531
6 · The paper itself

Abstract

This paper describes a data-driven simulation study that explores the relative impact of several low-cost and practical non-pharmaceutical interventions on the spread of COVID-19 in an outpatient hospital dialysis unit. The interventions considered include: (i) voluntary self-isolation of healthcare personnel (HCPs) with symptoms; (ii) a program of active syndromic surveillance and compulsory isolation of HCPs; (iii) the use of masks or respirators by patients and HCPs; (iv) improved social distancing among HCPs; (v) increased physical separation of dialysis stations; and (vi) patient isolation combined with preemptive isolation of exposed HCPs. Our simulations show that under conditions that existed prior to the COVID-19 outbreak, extremely high rates of COVID-19 infection can result in a dialysis unit. In simulations under worst-case modeling assumptions, a combination of relatively inexpensive interventions such as requiring surgical masks for everyone, encouraging social distancing between healthcare professionals (HCPs), slightly increasing the physical distance between dialysis stations, and-once the first symptomatic patient is detected-isolating that patient, replacing the HCP having had the most exposure to that patient, and relatively short-term use of N95 respirators by other HCPs can lead to a substantial reduction in both the attack rate and the likelihood of any spread beyond patient zero. For example, in a scenario with R0 = 3.0, 60% presymptomatic viral shedding, and a dialysis patient being the infection source, the attack rate falls from 87.8% at baseline to 34.6% with this intervention bundle. Furthermore, the likelihood of having no additional infections increases from 6.2% at baseline to 32.4% with this intervention bundle.

Indexed as

Ambulatory Care FacilitiesOutpatientsRenal DialysisCOVID-19HumansKidney DiseasesPatient IsolationSARS-CoV-2

Identifiers

PMID34237062
PMCPMC8291695
OpenAlexW3182282966

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.