Evidence map›Paper›PMID 37460598›Full record

ArticleScientific reports2023

Estimating generation time of SARS-CoV-2 variants in Italy from the daily incidence rate.

Eugenio Lippiello, Giuseppe Petrillo, Silvio Baccari, Lucilla de Arcangelis

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 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

4 authors.

Eugenio LippielloDepartment of Mathematics and Physics, University of Campania "L. Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy. eugenio.lippiello@unicampania.it.
Giuseppe PetrilloThe Institute of Statistical Mathematics, Research Organization of Information and Systems, Tokyo, Japan.
Silvio BaccariDepartment of Mathematics and Physics, University of Campania "L. Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy.
Lucilla de ArcangelisDepartment of Mathematics and Physics, University of Campania "L. Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of the transmission parameters of a virus is fundamental to identify the optimal public health strategy. These parameters can present significant changes over time caused by genetic mutations or viral recombination, making their continuous monitoring fundamental. Here we present a method, suitable for this task, which uses as unique information the daily number of reported cases. The method is based on a time since infection model where transmission parameters are obtained by means of an efficient maximization procedure of the likelihood. Applying the method to SARS-CoV-2 data in Italy, we find an average generation time [Formula: see text] days, during the temporal window when the majority of infections can be attributed to the Omicron variants. At the same time we find a significantly larger value [Formula: see text] days, in the temporal window when spreading was dominated by the Delta variant. We are also able to show that the presence of the Omicron variant, characterized by a shorter [Formula: see text], was already detectable in the first weeks of December 2021, in full agreement with results provided by sequences of SARS-CoV-2 genomes reported in national databases. Our results therefore show that the novel approach can indicate the existence of virus variants, resulting particularly useful in situations when information about genomic sequencing is not yet available. At the same time, we find that the standard deviation of the generation time does not significantly change among variants.

Indexed as

COVID-19SARS-CoV-2HumansIncidenceItaly

Identifiers

PMID37460598
PMCPMC10352265

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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.