For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
9
views
38
references
 Top references
cited by
2
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
1 collections
    0
    shares
      319
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      A network analysis and support vector regression approach for visualising and predicting the COVID-19 outbreak in Malaysia

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This study aims to (1) correlate and visualize the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualizing the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: not found

          Temporal dynamics in viral shedding and transmissibility of COVID-19

          Xi. He, Eric Lau, Peng Wu … (2020)
          We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector-infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 25-69%) of secondary cases were infected during the index cases' presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission.
          Article 0 comments Cited 2174 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Characteristics of SARS-CoV-2 and COVID-19

            Ben Hu, Hua Guo, Peng Zhou … (2020)
            Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible and pathogenic coronavirus that emerged in late 2019 and has caused a pandemic of acute respiratory disease, named ‘coronavirus disease 2019’ (COVID-19), which threatens human health and public safety. In this Review, we describe the basic virology of SARS-CoV-2, including genomic characteristics and receptor use, highlighting its key difference from previously known coronaviruses. We summarize current knowledge of clinical, epidemiological and pathological features of COVID-19, as well as recent progress in animal models and antiviral treatment approaches for SARS-CoV-2 infection. We also discuss the potential wildlife hosts and zoonotic origin of this emerging virus in detail.
            Article 0 comments Cited 1911 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              User's guide to correlation coefficients

              Haldun Akoglu (2018)
              When writing a manuscript, we often use words such as perfect, strong, good or weak to name the strength of the relationship between variables. However, it is unclear where a good relationship turns into a strong one. The same strength of r is named differently by several researchers. Therefore, there is an absolute necessity to explicitly report the strength and direction of r while reporting correlation coefficients in manuscripts. This article aims to familiarize medical readers with several different correlation coefficients reported in medical manuscripts, clarify confounding aspects and summarize the naming practices for the strength of correlation coefficients.
              Article 0 comments Cited 996 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Title: Healthcare Analytics
                Publisher: The Author(s). Published by Elsevier Inc.
                ISSN (Print): 2772-4425
                ISSN (Electronic): 2772-4425
                Publication date PMC-release: 19 July 2022
                Publication date (Electronic): 19 July 2022
                Electronic Location Identifier: 100080
                Affiliations
                [a ]Halal Products Research Institute, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
                [b ]Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
                [c ]International Institute for Halal Research and Training, International Islamic University Malaysia, Level 3, KICT Building, 53100 Kuala Lumpur, Malaysia
                [d ]Konsortium Institut Halal IPT Malaysia, Ministry of Higher Education, Block E8, Complex E, Federal Government Administrative Centre, 62604 Putrajaya, Malaysia
                [e ]The Catalytixs Solutions, No. 713, Jalan DPP 1/4, Desa Permai Pedas, 71400 Pedas, Negeri Sembilan, Malaysia
                Author notes
                [* ]Corresponding author at: International Institute for Halal Research and Training, International Islamic University Malaysia, Level 3, KICT Building, 53100 Kuala Lumpur, Malaysia.
                Article
                Publisher Item ID: S2772-4425(22)00033-8 Publisher ID: 100080
                DOI: 10.1016/j.health.2022.100080
                PMC ID: 9293790
                SO-VID: e3e87023-693f-4592-948d-40953ef429eb
                Copyright © © 2022 The Author(s)
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 27 April 2022
                Date revision received : 13 July 2022
                Date accepted : 14 July 2022
                Categories
                Subject: Article

                Keywords: coronavirus,covid-19,network analysis,support vector regression,artificial intelligence
                Data availability:
                Keywords: coronavirus, covid-19, network analysis, support vector regression, artificial intelligence

                Comments

                Comment on this article

                scite_

                Similar content319

                See all similar

                Cited by2

                See all cited by

                Most referenced authors2,678

                See all reference authors