Evidence map›Paper›PMID 33937596›Full record

ArticleInfectious Disease Modelling2021

Social distancing and testing as optimal strategies against the spread of COVID-19 in the Rio Grande Valley of Texas.

Kristina P Vatcheva, Josef Sifuentes, Tamer Oraby, Jose Campo Maldonado, Timothy Huber, María Cristina Villalobos

Open access · goldAbstract 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 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 41% 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, 6 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 1 institution in 1 country.

Kristina P VatchevaSchool of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, Brownsville, TX, 78520, USA.
Josef SifuentesSchool of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA.
Tamer OrabySchool of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA.
Jose Campo MaldonadoSchool of Medicine, The University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA.
Timothy HuberSchool of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA.
María Cristina VillalobosSchool of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA.
The University of Texas Rio Grande Valley · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

At the beginning of August 2020, the Rio Grande Valley (RGV) of Texas experienced a rapid increase of coronavirus disease 2019 (abbreviated as COVID-19) cases and deaths. This study aims to determine the optimal levels of effective social distancing and testing to slow the virus spread at the outset of the pandemic. We use an age-stratified eight compartment epidemiological model to depict COVID-19 transmission in the community and within households. With a simulated 120-day outbreak period data we obtain a post 180-days period optimal control strategy solution. Our results show that easing social distancing between adults by the end of the 180-day period requires very strict testing a month later and then daily testing rates of 5% followed by isolation of positive cases. Relaxing social distancing rates in adults from 50% to 25% requires both children and seniors to maintain social distancing rates of 50% for nearly the entire period while maintaining maximum testing rates of children and seniors for 150 of the 180 days considered in this model. Children have higher contact rates which leads to transmission based on our model, emphasizing the need for caution when considering school reopenings.

Indexed as

COVID-19Mathematical modelingOptimal controlRio Grande Valley (RGV)School reopeningTesting and social distancing

Identifiers

PMID33937596
PMCPMC8065238
OpenAlexW3158079752

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.