Evidence map›Paper›PMID 37162815›Full record

ArticleGlobal epidemiology2023

Estimating time-dependent contact: a multi-strain epidemiological model of SARS-CoV-2 on the island of Ireland.

Tsukushi Kamiya, Alberto Alvarez-Iglesias, John Ferguson, Shane Murphy, Mircea T Sofonea, Nicola Fitz-Simon

Abstract read
In one paragraph

Article in Global epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. A New Compartment Model of COVID-19 Transmission: The Broken-Link Model.International journal of environmental research and public health · 2022
    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.

Tsukushi KamiyaHRB Clinical Research Facility, University of Galway, Ireland.
Alberto Alvarez-IglesiasHRB Clinical Research Facility, University of Galway, Ireland.
John FergusonHRB Clinical Research Facility, University of Galway, Ireland.
Shane MurphyHRB Clinical Research Facility, University of Galway, Ireland.
Mircea T SofoneaMIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France.
Nicola Fitz-SimonHRB Clinical Research Facility, University of Galway, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mathematical modelling plays a key role in understanding and predicting the epidemiological dynamics of infectious diseases. We construct a flexible discrete-time model that incorporates multiple viral strains with different transmissibilities to estimate the changing patterns of human contact that generates new infections. Using a Bayesian approach, we fit the model to longitudinal data on hospitalisation with COVID-19 from the Republic of Ireland and Northern Ireland during the first year of the pandemic. We describe the estimated change in human contact in the context of government-mandated non-pharmaceutical interventions in the two jurisdictions on the island of Ireland. We take advantage of the fitted model to conduct counterfactual analyses exploring the impact of lockdown timing and introducing a novel, more transmissible variant. We found substantial differences in human contact between the two jurisdictions during periods of varied restriction easing and December holidays. Our counterfactual analyses reveal that implementing lockdowns earlier would have decreased subsequent hospitalisation substantially in most, but not all cases, and that an introduction of a more transmissible variant - without necessarily being more severe - can cause a large impact on the health care burden.

Identifiers

PMID37162815
PMCPMC10159265

What OpenQuestion holds

Textmetadata
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.