Public Response, Anxiety and Behaviour during the First Wave of COVID-19 Pandemic in Saudi Arabia

Alqahtani, Amani S.; Alrasheed, Meshael; Alqunaibet, Ada

doi:10.3390/ijerph18094628

Cited by 10 publications

(15 citation statements)

References 24 publications

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Section: General Description Of the Included Studiesmentioning

confidence: 99%

“…A total of 16 studies were included in this review, which consists of a total of 262,656 participants (ranging from 148 [Zakout et al, 2020] to 204,186 [Alqahtani et al, 2021]). The adult population was considered in all included studies, except one (i.e., ≥16 years [Alqahtani et al, 2021]). In most studies, half of the participants were females; however, the lowest and highest female participants were seen in Alqahtani et al (2021) and Hawash et al (2021).…”

Section: General Description Of the Included Studiesmentioning

confidence: 99%

“…The adult population was considered in all included studies, except one (i.e., ≥16 years [Alqahtani et al, 2021]). In most studies, half of the participants were females; however, the lowest and highest female participants were seen in Alqahtani et al (2021) and Hawash et al (2021). Only five studies reported mean age, where participants are young adults in the rest of the studies.…”

Section: General Description Of the Included Studiesmentioning

confidence: 99%

“…Three studies did not report any sampling methods (AlHusseini et al, 2021;Alotaibi & Boukelia, 2021;Alsaif et al, 2021). The study with the largest sample was considered a non-proportional quota sampling strategy (Alqahtani et al, 2021), where it is either convenient or snowball in the rest of the studies. However, the description of the recruited articles can be found in Table 1.…”

Section: General Description Of the Included Studiesmentioning

confidence: 99%

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Prevalence and factors associated with mental health problems in Saudi general population during the coronavirus disease 2019 pandemic: A systematic review and meta‐analysis

et al. 2022

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This systematic review and meta-analysis generates evidence of the prevalence and associated factors of common mental disorders (i.e., depression, anxiety, and stress) related to the pandemic among the Saudi general population. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive literature search was performed in the respective databases (e.g., PubMed, Scopus, Web of Science), from 22 July to 17 August 2021, and identified a total of 220 articles. Adhering to the inclusion criteria (i.e., original research concerning the prevalence and/or associated factors of depression and/or anxiety and/or stress disorders among the general Saudi population, published in English peer-reviewed journals), 15 studies were included in this review which consisted of a total of 262,656 participants. The overall prevalence of depression, anxiety, and stress was 30% (95% confidence interval [CI]: 22% to 38%, I 2 = 99.58%), 20% (95% CI: 16% to 24%, I 2 = 99.32%), and 29% (95% CI: 11% to 47%, I 2 = 99.76%), respectively. Risk factors of mental health problems were found to be female sex, younger age group, single/ divorced marital status, lower education, lower income, non-Saudis, unemployment status, students, being in a small family and living with elderly of the sociodemographic factors. Smokers, less physical activities, lower resilience, reduced immune status, chronic health problems, and psychiatric illness history were associated with a higher degree of mental health problems. In addition, coronavirus disease 2019 (COVID-19)-related risk factors of mental illness included lack of knowledge, fear, worry and concern, family member or friends' infection or death, lockdown restrictions, quarantine, confirmed or suspected of COVID-19 infection, and even pandemic effects. In conclusion, appropriate mental health preventive approaches for the Saudi general people are highly needed, where this review can be worthy of help by providing in detailed information to the respective authorities.

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Section: General Description Of the Included Studiesmentioning

confidence: 99%

Section: General Description Of the Included Studiesmentioning

confidence: 99%

Section: General Description Of the Included Studiesmentioning

confidence: 99%

Section: General Description Of the Included Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Prevalence and factors associated with mental health problems in Saudi general population during the coronavirus disease 2019 pandemic: A systematic review and meta‐analysis

et al. 2022

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“…For example, in Saudi Arabia, the first case of COVID-19 was reported on 2 March 2020 [67]. With the end of December 2020, the first wave of COVID-19 spread was almost ended as reported in [68][69][70]. Therefore, during the studying period from 2 March 2020 until 27 December 2020, there was only one COVID-19 spread wave in Saudi Arabia, as reported in the literature.…”

Section: Methodsmentioning

confidence: 71%

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Shahin

Almotairi

2021

Fractal Fract

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The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

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Development of the Leading Sustainable and Viable e-Government Concept in the Post-COVID Era

Goloshchapova

Skornichenko

Turgaeva

2023

Springer Proceedings in Business and Economics

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Public Response, Anxiety and Behaviour during the First Wave of COVID-19 Pandemic in Saudi Arabia

Cited by 10 publications

References 24 publications

Prevalence and factors associated with mental health problems in Saudi general population during the coronavirus disease 2019 pandemic: A systematic review and meta‐analysis

Prevalence and factors associated with mental health problems in Saudi general population during the coronavirus disease 2019 pandemic: A systematic review and meta‐analysis

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Development of the Leading Sustainable and Viable e-Government Concept in the Post-COVID Era

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