Evidence map›Paper›PMID 33402873›Full record

ArticleProceedings. Mathematical, physical, and engineering sciences2020

A statistical theory of the strength of epidemics: an application to the Italian COVID-19 case.

Gabriele Pisano, Gianni Royer-Carfagni

Abstract read
In one paragraph

Article in Proceedings. Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Gabriele PisanoConstruction Technologies Institute - Italian National Research Council (ITC-CNR), Viale Lombardia 49, 20098 San Giuliano Milanese, Milano, Italy.
Gianni Royer-CarfagniConstruction Technologies Institute - Italian National Research Council (ITC-CNR), Viale Lombardia 49, 20098 San Giuliano Milanese, Milano, Italy.ORCID 0000-0003-4879-9846

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proposed theory defines a relative index of epidemic lethality that compares any two configurations in different observation periods, preferably one in the acute and the other in a mild epidemic phase. Raw mortality data represent the input, with no need to recognize the cause of death. Data are categorized according to the victims' age, which must be renormalized because older people have a greater probability of developing a level of physical decay (human damage), favouring critical pathologies and co-morbidities. The probabilistic dependence of human damage on renormalized age is related to a death criterion considering a virus spread by contagion and our capacity to cure the disease. Remarkably, this is reminiscent of the Weibull theory of the strength of brittle structures containing a population of crack-like defects, in the correlation between the statistical distribution of cracks and the risk of fracture at a prescribed stress level. Age-of-death scaling laws are predicted in accordance with data collected in Italian regions and provinces during the first wave of COVID-19, taken as representative examples to validate the theory. For the prevention of spread and the management of the epidemic, the various parameters of the theory shall be informed on other existing epidemiological models.

Indexed as

COVID-19fracture mechanicsmathematical epidemiologyprobabilistic mechanicsWeibull statistics

Identifiers

PMID33402873
PMCPMC7776968

What OpenQuestion holds

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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.