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Author:

Sohail, Muhammad Tayyab (Sohail, Muhammad Tayyab.) | Yang, Minghui (Yang, Minghui.) | Maresova, Petra (Maresova, Petra.) | Mustafa, Sohaib (Mustafa, Sohaib.)

Indexed by:

SSCI Scopus SCIE

Abstract:

This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU*Education->PA-COVID, HI*Education->PA.COVID, SD*Education->PA.COVID, HRP*Education->PA.COVID, PM*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.

Keyword:

protective measures public awareness about COVID-19 public COVID-19 Pakistan health social distance SEM-ANN

Author Community:

  • [ 1 ] [Sohail, Muhammad Tayyab]Xiangtan Univ, Sch Publ Adm, Xiangtan, Peoples R China
  • [ 2 ] [Sohail, Muhammad Tayyab]Xiangtan Univ, South Asia Res Ctr, Sch Publ Adm, Xiangtan, Peoples R China
  • [ 3 ] [Yang, Minghui]Guangzhou City Univ Technol, Int Business Sch, Guangzhou, Peoples R China
  • [ 4 ] [Yang, Minghui]Guangdong Univ Foreign Studies, Res Ctr Accounting & Econ Dev Guangdong Hong Kong, Hong Kong, Peoples R China
  • [ 5 ] [Maresova, Petra]Univ Hradec Kralove, Fac Informat & Management, Hradec Kralove, Czech Republic
  • [ 6 ] [Maresova, Petra]Univ Teknol Malaysia, Malaysia Japan Int Inst Technol, Kuala Lumpur, Malaysia
  • [ 7 ] [Mustafa, Sohaib]Beijing Univ Technol, Coll Econ & Management, Beijing, Peoples R China

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Source :

FRONTIERS IN PUBLIC HEALTH

Year: 2022

Volume: 10

5 . 2

JCR@2022

5 . 2 0 0

JCR@2022

JCR Journal Grade:1

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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