Evidence map›Paper›PMID 32834645›Full record

ArticleChaos, solitons, and fractals2020

A minimal model of hospital patients' dynamics in COVID-19.

David Papo, Marco Righetti, Luciano Fadiga, Fabio Biscarini, Massimiliano Zanin

Open access · greenAbstract read
In one paragraph

Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Nonlinear time-series forecasts for decision support: short-term demand for ICU beds in Santiago, Chile, during the 2021 COVID-19 pandemic.International transactions in operational research : a journal of The International Federation of Operational Research Societies · 2022
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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 at 5 institutions in 3 countries.

David PapoCenter for Translational Neurophysiology for Speech and Communication, Fondazione Istituto Italiano di Tecnologia, via Fossato di Mortara 17/19, Ferrara 44121, Italy.
Marco RighettiEuropean Patent Office, Bayerstr. 34, Munich 80335, Germany.
Luciano FadigaCenter for Translational Neurophysiology for Speech and Communication, Fondazione Istituto Italiano di Tecnologia, via Fossato di Mortara 17/19, Ferrara 44121, Italy.
Fabio BiscariniCenter for Translational Neurophysiology for Speech and Communication, Fondazione Istituto Italiano di Tecnologia, via Fossato di Mortara 17/19, Ferrara 44121, Italy.
Massimiliano ZaninInstituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca 07122, Spain.
European Patent Organisation · DEInstitute for Cross-Disciplinary Physics and Complex Systems · ESItalian Institute of Technology · ITUniversity of Ferrara · ITUniversity of Modena and Reggio Emilia · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Italy has been one of the countries hardest hit by the coronavirus disease (COVID-19) pandemic. While the overall policy in response to the epidemic was to a large degree centralised, the regional basis of the healthcare system represented an important factor affecting the natural dynamics of the disease induced geographic specificities. Here, we characterise the region-specific modulation of COVID dynamics with a reduced exponential model leveraging available data on sub-intensive and intensive care unit patients made available by all regional councils from the very onset of the disease. This simple model provides a rather good fit of regional patient dynamics, particularly for regions where the affected population was large, highlighting important region-specific patterns of epidemic dynamics.

Indexed as

COVID-19Discrete-time modelPatients’ dynamics

Identifiers

PMID32834645
PMCPMC7386477
OpenAlexW3045676563

What OpenQuestion holds

Textmetadata
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.