Evidence map›Paper›PMID 36123382›Full record

ArticleScientific reports2022

Epidemiological model based periodic intervention policies for COVID-19 mitigation in the United Kingdom.

Gianmario Rinaldi, Prathyush P Menon, Antonella Ferrara, W David Strain, Christopher Edwards

Abstract read
In one paragraph

Article in Scientific reports, 2022. 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
–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

3 citing papers in PubMed.

  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

5 authors.

Gianmario RinaldiThe Centre for Future Clean Mobility, Department of Engineering, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX5 2GD, United Kingdom. G.Rinaldi3@exeter.ac.uk.
Prathyush P MenonThe Centre for Future Clean Mobility, Department of Engineering, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX5 2GD, United Kingdom.
Antonella FerraraDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100, Pavia, Italy.
W David StrainMedical School, University of Exeter, Exeter, EX1 2LU, United Kingdom.
Christopher EdwardsDepartment of Engineering, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the UK, together with numerous countries in the world, moves towards a new phase of the COVID-19 pandemic, there is a need to be able to predict trends in sufficient time to limit the pressure faced by the National Health Service (NHS) and maintain low hospitalisation levels. In this study, we explore the use of an epidemiological compartmental model to devise a periodic adaptive suppression/intervention policy to alleviate the pressure on the NHS. The proposed model facilitates the understanding of the progression of the specific stages of COVID-19 in communities in the UK including: the susceptible population, the infected population, the hospitalised population, the recovered population, the deceased population, and the vaccinated population. We identify the parameters of the model by relying on past data within the period from 1 October 2020 to 1 June 2021. We use the total number of hospitalised patients and the fraction of those infected who are being admitted to hospital to develop adaptive policies: these modulate the recommended level of social restriction measures and realisable vaccination target adjustments. The analysis over the period 1 October 2020 to 1 June 2021 demonstrates our periodic adaptive policies have the potential to reduce the hospitalisation by 58% on average per month. In a further prospective analysis over the period August 2021 to May 2022, we analyse several future scenarios, characterised by the relaxation of restrictions, the vaccination ineffectiveness and the gradual decay of the vaccination-induced immunity within the population. In addition, we simulate the surge of plausible variants characterised by an higher transmission rate. In such scenarios, we show that our periodic intervention is effective and able to maintain the hospitalisation rate to a manageable level.

Indexed as

Communicable Disease ControlCOVID-19Epidemiological ModelsHealth PolicyHumansPandemicsState MedicineUnited Kingdom

Identifiers

PMID36123382
PMCPMC9483909

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Registered trials

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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.