Evidence map›Paper›PMID 32876321›Full record

ArticleRevista da Sociedade Brasileira de Medicina Tropical2020

Long-term forecasts of the COVID-19 epidemic: a dangerous idea.

Edson Zangiacomi Martinez, Davi Casale Aragon, Altacílio Aparecido Nunes

Abstract read
In one paragraph

Article in Revista da Sociedade Brasileira de Medicina Tropical, 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
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Feasibility of very short-term forecast models for COVID-19 hospital-based surveillance.Revista da Sociedade Brasileira de Medicina Tropical · 2021
    Article
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.

Edson Zangiacomi MartinezFaculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brasil.
Davi Casale AragonFaculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brasil.
Altacílio Aparecido NunesFaculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brasil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMathematical models have been used to obtain long-term forecasts of the COVID-19 epidemic.

methodsThe daily COVID-19 case count in two Brazilian states was used to show the potential limitations of long-term forecasting through the application of a mathematical model to the data.

resultsThe predicted number of cases at the end of the epidemic and at the moment that the peak occurs, is highly dependent on the length of the time series used in the predictive model.

conclusionsPredictions obtained during the course of the COVID-19 pandemic need to be viewed with caution.

Indexed as

CoronavirusPandemicsBetacoronavirusCoronavirus InfectionsCOVID-19ForecastingHumansModels, StatisticalPneumonia, ViralSARS-CoV-2

Identifiers

PMID32876321
PMCPMC7451501

What OpenQuestion holds

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LicenceCC BY
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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.