Evidence map›Paper›PMID 33489297›Full record

ArticleRoyal Society open science2020

SI epidemic model applied to COVID-19 data in mainland China.

J Demongeot, Q Griette, P Magal

Abstract read
In one paragraph

Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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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

3 authors.

J DemongeotDepartment of Medicine, Université Grenoble Alpes, AGEIS EA7407, 38700 La Tronche, France.
Q GrietteDepartment of Medicine, University of Bordeaux, IMB, UMR, 5251, 33400 Talence, France.
P MagalDepartment of Medicine, University of Bordeaux, IMB, UMR, 5251, 33400 Talence, France.ORCID 0000-0002-4776-0061

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit to the early cumulative data of SARS-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.

Indexed as

corona virusepidemic mathematical modelparameters identificationreported and unreported cases

Identifiers

PMID33489297
PMCPMC7813244

What OpenQuestion holds

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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.