Literature DB >> 34163172

Structural Zero Data of COVID-19 Discovers Exodus Probabilities.

Ramalingam Shanmugam1, Karan P Singh2.   

Abstract

BACKGROUND: Challenges to manage, mitigate, or prevent the COVID-19's pandemics are felt by medical, healthcare professionals and governing agencies. Health researchers conduct survey among the citizens to capture their opinion on COVID-19. In such surveys like in Hanafiah and Wan (2020), structural-zero (different from sampling zero) category occurs as they question about perception, knowledge, and communication regarding COVID-19. MATERIALS: The data were collected in a survey conducted among Malaysians by Hanafiah and Wan regarding COVID-19. The survey focused on people's response about the public communication, knowledge, and perception.
METHODS: One of the four question categories in the survey is mutually exclusive with the other three questions. Consequently, there will be no entry in that category. Such group is called structurally zero category in the literature. The literature never probed the migrative split to other categories of the unknown proportion belonging to the structural zero category. In this article, the probability-based new and innovative method configures what proportion in that mutually exclusive category and it is the essence of our method.
RESULTS: The mutually exclusive nature of subquestions manufactured structural zero in their data. A careful analysis of the data has created so far unknown probability concepts in the literature, which we named as "Exodus probabilities" in this article. Its discovery and utility are illustrated and elaborated with application in COVID-19. This methodology is also useful in applications in engineering, epidemiology, marketing, communication networking, etc.
CONCLUSION: What is quite novel about the discovery of the exodus probability in this article is the evolution of the concepts from the structural-zero category. In such situation, when a category is eliminated, the proportions of the sample might have uncommunicatively transited to other viable categories and our research question is all about configuring their proportions. This is an innovative approach.
© 2021 Shanmugam and Singh.

Entities:  

Keywords:  Venn layout; communication; dependent probabilities; odds; odds ratio; perception; public knowledge; sample space; survey outcomes

Year:  2021        PMID: 34163172      PMCID: PMC8214563          DOI: 10.2147/JMDH.S304419

Source DB:  PubMed          Journal:  J Multidiscip Healthc        ISSN: 1178-2390


  8 in total

Review 1.  Patterns and associated factors of COVID-19 knowledge, attitude, and practice among general population and health care workers: A systematic review.

Authors:  Firomsa Bekele; Tadesse Sheleme; Ginenus Fekadu; Kumera Bekele
Journal:  SAGE Open Med       Date:  2020-11-11

2.  Fighting fake news in the COVID-19 era: policy insights from an equilibrium model.

Authors:  Kris Hartley; Minh Khuong Vu
Journal:  Policy Sci       Date:  2020-09-09

3.  Importation and Human-to-Human Transmission of a Novel Coronavirus in Vietnam.

Authors:  Lan T Phan; Thuong V Nguyen; Quang C Luong; Thinh V Nguyen; Hieu T Nguyen; Hung Q Le; Thuc T Nguyen; Thang M Cao; Quang D Pham
Journal:  N Engl J Med       Date:  2020-01-28       Impact factor: 91.245

4.  Survey data of COVID-19-related knowledge, attitude, and practices among indonesian undergraduate students.

Authors:  Muhammad Saefi; Ahmad Fauzi; Evi Kristiana; Widi Cahya Adi; M Muchson; M Eval Setiawan; Novita Nurul Islami; Dian Eka Aprilia Fitria Ningrum; M Alifudin Ikhsan; Mavindra Ramadhani
Journal:  Data Brief       Date:  2020-06-12

5.  The impact of gender on emotional reactions, perceived susceptibility and perceived knowledge about COVID-19 among the Israeli public.

Authors:  Inbar Levkovich; Shiri Shinan-Altman
Journal:  Int Health       Date:  2021-12-01       Impact factor: 2.473

6.  A Novel Coronavirus from Patients with Pneumonia in China, 2019.

Authors:  Na Zhu; Dingyu Zhang; Wenling Wang; Xingwang Li; Bo Yang; Jingdong Song; Xiang Zhao; Baoying Huang; Weifeng Shi; Roujian Lu; Peihua Niu; Faxian Zhan; Xuejun Ma; Dayan Wang; Wenbo Xu; Guizhen Wu; George F Gao; Wenjie Tan
Journal:  N Engl J Med       Date:  2020-01-24       Impact factor: 91.245

7.  Community's perceived high risk of coronavirus infections during early phase of epidemics are significantly influenced by socio-demographic background, in Gondar City, Northwest Ethiopia: A cross-sectional -study.

Authors:  Gebisa Guyasa Kabito; Mekuriaw Alemayehu; Tesfaye Hambisa Mekonnen; Sintayehu Daba Wami; Jember Azanaw; Tsegaye Adane; Zelalem Nigussie Azene; Mehari Woldemariam Merid; Atalay Goshu Muluneh; Demiss Mulatu Geberu; Getahun Molla Kassa; Melaku Kindie Yenit; Sewbesew Yitayih Tilahun; Kassahun Alemu Gelaye; Habtamu Sewunet Mekonnen; Abere Woretaw Azagew; Chalachew Adugna Wubneh; Getaneh Mulualem Belay; Nega Tezera Assimamaw; Chilot Desta Agegnehu; Telake Azale; Animut Tagele Tamiru; Bayew Kelkay Rade; Eden Bishaw Taye; Asefa Adimasu Taddese; Zewudu Andualem; Henok Dagne; Kiros Terefe Gashaye
Journal:  PLoS One       Date:  2020-11-19       Impact factor: 3.240

8.  Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020.

Authors:  Jantien A Backer; Don Klinkenberg; Jacco Wallinga
Journal:  Euro Surveill       Date:  2020-02
  8 in total

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