Evidence map›Paper›PMID 35035627›Full record

ArticleProcedia computer science2022

COVID-19 Time Series Forecasting - Twenty Days Ahead.

Kathleen C M de Carvalho, João Paulo Vicente, João Paulo Teixeira

Abstract read
In one paragraph

Article in Procedia computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Kathleen C M de CarvalhoResearch Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politecnico de Bragança, Braganca, Portugal.
João Paulo VicenteResearch Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politecnico de Bragança, Braganca, Portugal.
João Paulo TeixeiraResearch Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politecnico de Bragança, Braganca, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The new Coronavirus, responsible for the COVID-19 disease, is the most discussed topic in the current days, and the forecast numbers of new cases and deaths are the most important source of data in governmental decision-making. The present work presents a prediction model with two different approaches concerning the input data, by using Artificial Neural Networks (ANN). The use of a substantial mitigation procedure adopted (mandatory use of masks) was experimented as an input to the network, in order to evaluate the improvement in the results. The ANN forecasting model was demonstrated to predict with higher accuracy within the next twenty days using the information about the mandatory use of face masks. The final results showed that the twenty days ahead forecasting was made with an error of 24,7% and 1,6% for the number of cumulative cases of infection and deaths for Brazil, and 37,9% and 33,8% for Portuguese time series, respectively.

Indexed as

Brazil COVID-19 forecastingCOVID-19 time seriesMitigation proceduresPortugal COVID-19 forecastingUse of face masks

Identifiers

PMID35035627
PMCPMC8745940

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.