Evidence map›Paper›PMID 34613981›Full record

ArticlePloS one2021

Smart testing and critical care bed sharing for COVID-19 control.

Paulo J S Silva, Tiago Pereira, Claudia Sagastizábal, Luis Nonato, Marcelo M Cordova, Claudio J Struchiner

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Paulo J S SilvaInstituto de Matemática, Estatística e Computação Científica, Universidade de Campinas, São Paulo, Brazil.
Tiago PereiraInstituto de Ciências Matemáticas e Computação, Universidade de São Paulo, São Paulo, Brazil.ORCID 0000-0001-5934-4375
Claudia SagastizábalInstituto de Matemática, Estatística e Computação Científica, Universidade de Campinas, São Paulo, Brazil.
Luis NonatoInstituto de Ciências Matemáticas e Computação, Universidade de São Paulo, São Paulo, Brazil.
Marcelo M CordovaDepartamento de Engenharia Elétrica, Universidade Federal de Santa Catarina, Florianópolis, Brazil.
Claudio J StruchinerFundação Getúlio Vargas, Rio de Janeiro, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.

Indexed as

COVID-19Intensive Care UnitsSARS-CoV-2Bed OccupancyBrazilCOVID-19 TestingCritical CareHumansPandemics

Identifiers

PMID34613981
PMCPMC8494319

What OpenQuestion holds

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LicenceCC BY
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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.