Evidence map›Paper›PMID 35996714›Full record

ArticleChaos, solitons, and fractals2022

Data-driven approach in a compartmental epidemic model to assess undocumented infections.

Guilherme S Costa, Wesley Cota, Silvio C Ferreira

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

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
0.1field-weighted citation impact, top 62% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

3 authors at 1 institution in 1 country.

Guilherme S CostaDepartamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil.
Wesley CotaDepartamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil.
Silvio C FerreiraDepartamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil.
Universidade Federal de Viçosa · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowcasting and forecasting of epidemic spreading rely on incidence series of reported cases to derive the fundamental epidemiological parameters for a given pathogen. Two relevant drawbacks for predictions are the unknown fractions of undocumented cases and levels of nonpharmacological interventions, which span highly heterogeneously across different places and times. We describe a simple data-driven approach using a compartmental model including asymptomatic and pre-symptomatic contagions that allows to estimate both the level of undocumented infections and the value of effective reproductive number

Indexed as

Data-driven modelingEpidemic spreadingEpidemic surveillanceUndocumented infections

Identifiers

PMID35996714
PMCPMC9385215
OpenAlexW4223959023

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.