Evidence map›Paper›PMID 34575666›Full record

ArticleJournal of personalized medicine2021

COVID-19 Pandemic Outbreak in the Subcontinent: A Data Driven Analysis.

Bikash Chandra Singh, Zulfikar Alom, Haibo Hu, Mohammad Muntasir Rahman, Mrinal Kanti Baowaly, Zeyar Aung, Mohammad Abdul Azim, Mohammad Ali Moni

Abstract read
In one paragraph

Article in Journal of personalized medicine, 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
  2. 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

8 authors.

Bikash Chandra SinghDepartment of Information and Communication Technology, Islamic University, Kushtia 7003, Bangladesh.ORCID 0000-0002-2870-8137
Zulfikar AlomDepartment of Computer Science, Asian University for Women (AUW), Chattagram 4000, Bangladesh.
Haibo HuDepartment of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Mohammad Muntasir RahmanDepartment of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh.ORCID 0000-0003-2233-9251
Mrinal Kanti BaowalyDepartment of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh.
Zeyar AungDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, United Arab Emirates.ORCID 0000-0001-5990-9305
Mohammad Abdul AzimDepartment of Computer Science, Asian University for Women (AUW), Chattagram 4000, Bangladesh.ORCID 0000-0001-5529-9482
Mohammad Ali MoniSchool of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent.

Indexed as

coronavirusCOVID-19 pandemicmodelling infectious diseasereproduction numberSARS-COV-2

Identifiers

PMID34575666
PMCPMC8467040

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.