Evidence map›Paper›PMID 34462450›Full record

ArticleScientific reports2021

Functional data analysis characterizes the shapes of the first COVID-19 epidemic wave in Italy.

Tobia Boschi, Jacopo Di Iorio, Lorenzo Testa, Marzia A Cremona, Francesca Chiaromonte

Abstract readMulticenter Study
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

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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

5 authors.

Tobia BoschiDept. of Statistics and Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA.
Jacopo Di IorioInstitute of Economics and EMbeDS, Sant'Anna School of Advanced Studies, 56127, Pisa, Italy.
Lorenzo TestaInstitute of Economics and EMbeDS, Sant'Anna School of Advanced Studies, 56127, Pisa, Italy.
Marzia A CremonaDept. of Statistics and Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA. marzia.cremona@fsa.ulaval.ca.
Francesca ChiaromonteDept. of Statistics and Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA. fxc11@psu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate patterns of COVID-19 mortality across 20 Italian regions and their association with mobility, positivity, and socio-demographic, infrastructural and environmental covariates. Notwithstanding limitations in accuracy and resolution of the data available from public sources, we pinpoint significant trends exploiting information in curves and shapes with Functional Data Analysis techniques. These depict two starkly different epidemics; an "exponential" one unfolding in Lombardia and the worst hit areas of the north, and a milder, "flat(tened)" one in the rest of the country-including Veneto, where cases appeared concurrently with Lombardia but aggressive testing was implemented early on. We find that mobility and positivity can predict COVID-19 mortality, also when controlling for relevant covariates. Among the latter, primary care appears to mitigate mortality, and contacts in hospitals, schools and workplaces to aggravate it. The techniques we describe could capture additional and potentially sharper signals if applied to richer data.

Indexed as

EpidemicsCOVID-19Data AnalysisHumansItalyPopulation Dynamics

Identifiers

PMID34462450
PMCPMC8405612

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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.