Evidence map›Paper›PMID 34482186›Full record

ArticleEpidemics2021

COVID-19 Belgium: Extended SEIR-QD model with nursing homes and long-term scenarios-based forecasts.

Nicolas Franco

Abstract read
In one paragraph

Article in Epidemics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

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0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
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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

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3 · Its place in the literature

Who cites it

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

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

1 author.

Nicolas FrancoNamur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium; Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat) and Data Science Institute, University of Hasselt, Hasselt, Belgium. Electronic address: nicolas.franco@unamur.be.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Following the spread of the COVID-19 pandemic and pending the establishment of vaccination campaigns, several non pharmaceutical interventions such as partial and full lockdown, quarantine and measures of physical distancing have been imposed in order to reduce the spread of the disease and to lift the pressure on healthcare system. Mathematical models are important tools for estimating the impact of these interventions, for monitoring the current evolution of the epidemic at a national level and for estimating the potential long-term consequences of relaxation of measures. In this paper, we model the evolution of the COVID-19 epidemic in Belgium with a deterministic age-structured extended compartmental model. Our model takes special consideration for nursing homes which are modelled as separate entities from the general population in order to capture the specific delay and dynamics within these entities. The model integrates social contact data and is fitted on hospitalisations data (admission and discharge), on the daily number of COVID-19 deaths (with a distinction between general population and nursing home related deaths) and results from serological studies, with a sensitivity analysis based on a Bayesian approach. We present the situation as in November 2020 with the estimation of some characteristics of the COVID-19 deduced from the model. We also present several mid-term and long-term projections based on scenarios of reinforcement or relaxation of social contacts for different general sectors, with a lot of uncertainties remaining.

Indexed as

COVID-19Bayes TheoremBelgiumCommunicable Disease ControlEpidemiological ModelsHumansNursing HomesPandemicsSARS-CoV-2Age-structured compartmental SEIR modelHospitalisation and mortality dataMarkov Chain Monte Carlo (MCMC)SARS-CoV-2Social contact patterns

Identifiers

PMID34482186
PMCPMC8390100

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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.