Modeling the multifractal dynamics of COVID-19 pandemic.

V P Tsvetkov, S A Mikheev, I V Tsvetkov, V L Derbov, A A Gusev, S I Vinitsky

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Article in Chaos, solitons, and fractals, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

1.2field-weighted citation impact, top 24% 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

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3 · Its place in the literature

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0 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

V P TsvetkovTver State University, 33, Zhelyabova St., Tver 170100, Russia.

S A MikheevTver State University, 33, Zhelyabova St., Tver 170100, Russia.

I V TsvetkovTver State University, 33, Zhelyabova St., Tver 170100, Russia.

V L DerbovN.G. Chernyshevsky Saratov National Research State University, Saratov, Russia.

A A GusevJoint Institute for Nuclear Research, Dubna, Russia.

S I VinitskyJoint Institute for Nuclear Research, Dubna, Russia.

Tver State University · RUJoint Institute for Nuclear Research · RUPeoples' Friendship University of Russia · RUSaratov State University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To describe the COVID-19 pandemic, we propose to use a mathematical model of multifractal dynamics, which is alternative to other models and free of their shortcomings. It is based on the fractal properties of pandemics only and allows describing their time behavior using no hypotheses and assumptions about the structure of the disease process. The model is applied to describe the dynamics of the COVID-19 pandemic from day 1 to day 699 from the beginning of the pandemic. The calculated parameters of the model accurately determine the parameters of the trend and the large jump in daily diseases in this time interval. Within the framework of this model and finite-difference parametric nonlinear equations of the reduced SIR (Susceptible-Infected-Removed) model, the fractal dimensions of various segments of daily incidence in the world and variations in the main reproduction number of COVID-19 were calculated based on the data of COVID-19 world statistics.

Indexed as

COVID-19 pandemicFinite-difference parametric nonlinear equationsMathematical modelMultifractal dynamicsReduced SIR model

Identifiers

PMID35755146

DOI10.1016/j.chaos.2022.112301

PMCPMC9212712

OpenAlexW4283079356

What OpenQuestion holds

Textmetadata

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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.