Evidence map›Paper›PMID 34307058›Full record

ArticleMaterials today. Proceedings2023

Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units.

Sathish Natarajan, Mohit Kumar, Sai Kiran Kumar Gadde, Vijay Venugopal

Open access · greenAbstract read
In one paragraph

Article in Materials today. Proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026
    Article
  2. Article
  3. Prediction of COVID-19 Using a WOA-BILSTM Model.Bioengineering (Basel, Switzerland) · 2023
    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

4 authors at 3 institutions in 2 countries.

Sathish NatarajanCognizant Technology Solutions, Chennai, India.
Mohit KumarDepartment of Computer Science and Engineering, University Institute of Engineering, Chandigarh University, Mohali, Punjab, India.
Sai Kiran Kumar GaddeSenior Data Developer, Flagstar Bank, Troy, Michigan, United States.
Vijay VenugopalNetwork Consultant, Cisco Systems, China.
Chandigarh University · INCisco Systems (China) · CNCognizant (India) · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Respiratory infections corona virus 2-caused inflammatory disorders are CORONAVIRUS DISEASE 2019 (COVID-19) (SARS-CoV-2). A serious corona virus acute disease arose in 2019. Wuhan, China, was the first location to find the virus in December 2019, which has now been spreading all over the world. Recurrent neural networks, together with the use of LSTMs, fail to provide solutions to numerous issues (RNNs). So this paper has proposed RNN with Gated Recurrent Units for the COVID-19 prediction. This paper utilizes system, which was developed to assist nations (the Czech Republic, the United States, India, and Russia) combat the early stages of a newly emerging infection. For instance, the system tracks confirmed and reported cases, and monitors cures and deaths on a daily basis. This was done to allow the relevant parties to have an early grasp of the disastrous damage the lethal virus will bring. The implemented is an ensemble approach of RNN and GRU that work has computed the RMSE value for the different cases such as infected, cure and death across the four different countries.

Indexed as

Deep learningLSTM GRURecurrent neural networkRMSE

Identifiers

PMID34307058
PMCPMC8289676
OpenAlexW3185467239

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.