Evidence map›Paper›PMID 36848355›Full record

ArticlePloS one2023

Correcting the reproduction number for time-varying tests: A proposal and an application to COVID-19 in France.

Christelle Baunez, Mickaël Degoulet, Stéphane Luchini, Matteo L Pintus, Patrick A Pintus, Miriam Teschl

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 57% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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 at 2 institutions in 1 country.

Christelle BaunezAix-Marseille Univ, CNRS, Institut Neurosciences Timone, Marseille, France.
Mickaël DegouletAix-Marseille Univ, CNRS, Institut Neurosciences Timone, Marseille, France.
Stéphane LuchiniAix-Marseille Univ, CNRS, Aix-Marseille School of Economics, Marseille, France.
Matteo L PintusAgroParisTech and University Paris-Saclay, Paris, France.
Patrick A PintusAix-Marseille Univ, CNRS, Aix-Marseille School of Economics, Marseille, France.ORCID 0000-0003-2263-0655
Miriam TeschlAix-Marseille Univ, CNRS, EHESS, Aix-Marseille School of Economics, Marseille, France.
Centre National de la Recherche Scientifique · FRAgroParisTech · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We provide a novel way to correct the effective reproduction number for the time-varying amount of tests, using the acceleration index (Baunez et al., 2021) as a simple measure of viral spread dynamics. Not correcting results in the reproduction number being a biased estimate of viral acceleration and we provide a formal decomposition of the resulting bias, involving the useful notions of test and infectivity intensities. When applied to French data for the COVID-19 pandemic (May 13, 2020-October 26, 2022), our decomposition shows that the reproduction number, when considered alone, characteristically underestimates the resurgence of the pandemic, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all relevant information and captures in real time the sizable time variation featured by viral circulation, it is a more parsimonious indicator to track the dynamics of an infectious disease outbreak in real time, compared to the equivalent alternative which would combine the reproduction number with the test and infectivity intensities.

Indexed as

COVID-19AccelerationFranceHumansPandemicsReproduction

Identifiers

PMID36848355
PMCPMC9970098
OpenAlexW4225079413

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.