Evidence map›Paper›PMID 33613017›Full record

ArticleDigital signal processing2021

Stochastic filtering based transmissibility estimation of novel coronavirus.

Rahul Bansal, Amit Kumar, Amit Kumar Singh, Sandeep Kumar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Digital signal processing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Rahul BansalECE Department, Ajay Kumar Garg Engineering College, Ghaziabad, India.
Amit KumarCS Department, Dyal Singh College, University of Delhi, Delhi, India.
Amit Kumar SinghCS Department, Ramanujan College, University of Delhi, Delhi, India.
Sandeep KumarCentral Research Laboratory, BEL, Ghaziabad, Uttar Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, the transmissibility estimation of novel coronavirus (COVID-19) has been presented using the generalized fractional-order calculus (FOC) based extended Kalman filter (EKF) and wavelet transform (WT) methods. Initially, the state-space representation for the bats-hosts-reservoir-people (BHRP) model is obtained using a set of fractional order differential equations for the susceptible-exposed-infectious-recovered (SEIR) model. Afterward, the EKF and Kronecker product based WT methods have been applied to the discrete vector representation of the BHRP model. The main advantage of using EKF in this system is that it considers both the process and the measurement noise, which gives better accuracy and probable states, which are the Markovian (processes). The importance of proposed models lies in the fact that these models can accommodate conventional EKF and WT methods as their special cases. Further, we have compared the estimated number of contagious people and recovered people with the actual number of infectious people and recovered people in India and China.

Indexed as

Extended Kalman filterNovel coronavirusState space modelTransmissibility estimation

Identifiers

PMID33613017
PMCPMC7883689

What OpenQuestion holds

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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.