Evidence map›Paper›PMID 33841583›Full record

ArticleEvolutionary intelligence2022

A statistical and deep learning-based daily infected count prediction system for the coronavirus pandemic.

Vruddhi Shah, Ankita Shelke, Mamata Parab, Jainam Shah, Ninad Mehendale

Abstract read
In one paragraph

Article in Evolutionary intelligence, 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
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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

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.

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

5 authors.

Vruddhi ShahK. J. Somaiya College of Engineering, Mumbai, 400077 Maharashtra India.
Ankita ShelkeK. J. Somaiya College of Engineering, Mumbai, 400077 Maharashtra India.
Mamata ParabK. J. Somaiya College of Engineering, Mumbai, 400077 Maharashtra India.
Jainam ShahK. J. Somaiya College of Engineering, Mumbai, 400077 Maharashtra India.
Ninad MehendaleK. J. Somaiya College of Engineering, Mumbai, 400077 Maharashtra India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s12065-021-00600-2.

Indexed as

CoronavirusCovid-19 simulationsDaily count

Identifiers

PMID33841583
PMCPMC8019340

What OpenQuestion holds

Textmetadata
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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.