Measuring the impact of the COVID-19 epidemic on university resumption and suggestions for countermeasures

Yin, Shi; Ma, Lijun; Dong, Tong; Wang, Ying

doi:10.3389/fpubh.2022.1037818

Cited by 6 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address this, our study examines the dynamic changes in the overall integration networks of industrial sectors' information (IIN), as well as the unique characteristics of individual IIN and their impact on performance. Shi Yin et al [34] initially conducted a literature review to identify the primary factors influencing virus transmission. Subsequently, the pressure-state-response model was employed to illustrate the actions and interactions of these identified hazards, which were then categorized into disaster and vulnerability factors.…”

Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

Puttegowda,

DS,

Mallu

et al. 2024

TOPHJ

View full text Add to dashboard Cite

Background The development of technology has a significant impact on every aspect of life, whether it is the medical industry or any other profession. The potential of artificial intelligence has been demonstrated in data processing and analysis is used to inform decisions in the field of health care. The most crucial action is the early detection of a life-threatening illness to stop its development and spread. Highly contagious COVID-19 is a disease that requires immediate attention as it has spread globally. There is a need for a technology that can be utilised to detect the virus because of how quickly it spreads. With the increased use of technology, we now have access to a wealth of COVID-19-related information that may be used to learn crucial details about the virus. Objective The objective of the work is to develop comprehensible machine learning models for the automatic prediction of COVID-19. These models aim to accurately predict the likelihood of COVID-19 infection based on relevant input features, such as symptoms, demographics, and potential diagnostic tests or imaging results. Methods In this work, we mainly aimed to assess how well different machine learning methods might predict COVID-19 situations. In order to do this, we thoroughly evaluated a variety of widely used classifiers in machine learning. Popular algorithms like the random forest, k-nearest neighbour, and logistic regression were all included in our analysis. Results To assess the performance of our suggested algorithms using different machine learning techniques, we used an open-source dataset in the study. Our algorithms performed better than other models that are currently in use, which is noteworthy. The high degree of precision in predicting COVID-19 instances is demonstrated by our remarkable accuracy of 96.34%. We also obtained a good F1 score of 0.98 for our models, indicating the strength and efficiency of our method in obtaining metrics for both recall and precision. Conclusion This work highlights the possibility of understanding machine learning algorithms for COVID-19 prediction automatically. We have shown that techniques such as logistic regression, random forest, and k-nearest neighbor methods may reliably predict COVID-19 situations while preserving interpretability. In order to promote acceptance and confidence among healthcare professionals and enable well-informed decision-making in clinical settings, these models' transparency is essential. To improve these models' efficacy and scalability in the future, more research will be needed to enhance and validate them on a variety of datasets. In the end, utilizing understandable machine learning algorithms presents encouraging opportunities for COVID-19 early diagnosis and control, supporting international public health campaigns.

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

Puttegowda,

DS,

Mallu

et al. 2024

TOPHJ

View full text Add to dashboard Cite

show abstract

“…This shows that there is a significant negative correlation between risk perception and psychological resilience. Column (7) examines the correlation coefficient between psychological resilience and positive psychological capital. The correlation coefficient between psychological resilience and positive psychological capital is 0.27, which is significant at 1% significance level.…”

Section: Descriptive Statistics and Correlation Analysis Among Variablesmentioning

confidence: 99%

“…Under such circumstances, college students will be in a state of tension for a long time. Out of fear of the pandemic, it is easy to form a psychological stress disorder [7] .…”

Section: Introductionmentioning

confidence: 99%

The Impact of COVID-19 Risk Perception on College Students' Positive Psychological Capital: the Role of Psychological Resilience and Social Support

Ning,

Yin,

Zhang

2024

j. of psychol. res.

View full text Add to dashboard Cite

In order to explore the relationship between COVID-19 risk perception and positive psychological capital in college students, this study investigates the mediating role of psychological resilience and the moderating role of social support. Methods: In this study, 586 college students were surveyed by COVID-19 Risk Perception Scale, Positive Psychological Capital Questionnaire, Chinese Version of Resilience Scale and Social Support Scale. Results: (1) There is a significant negative correlation between COVID-19 risk perception and positive psychological capital, that is, COVID-19 risk perception can significantly and negatively predict the positive psychological capital of college students. (2) Psychological resilience plays a mediating role between risk perception and positive psychological capital. (3) Social support plays a weakening role in the negative prediction of risk perception on positive psychological capital. Conclusions: COVID-19 risk perception not only directly reduces positive psychological capital, but also affects positive psychological capital of college students through psychological resilience; social support can adjust the negative impact of risk perception on positive psychological capital. This study reveals the relationship between COVID-19 risk perception and college students' positive psychological capital and its mechanism, which has positive enlightenment significance for improving college students' mental health status and better promoting self-regulation during major public health events.

show abstract

“…The Pressure–State–Response model contends that the relationship between external pressure, individual state, and individual response behavior forms an interactive decision-making process. The influence of external pressure on individual behavior is realized through an individual’s current perception and psychological state, which are both affected by pressure ( 36 ). The COVID-19 pandemic is a typical public health emergency, and the public’s willingness to protect themselves from the epidemic is a typical stress response to changes in their own risk perception caused by the pressure of virus transmission and prevention policies; this is a typical stress response process.…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

Impact of epidemic prevention policies on public vaccination willingness: empirical research in China

Zhong,

Zhuang,

Zhang

2024

Front. Public Health

View full text Add to dashboard Cite

BackgroundThe sudden emergence of the COVID-19 pandemic in early 2020 posed an enormous threat to public health. Vaccination is currently recognized as the most cost-effective preventive and control measure against the COVID-19 pandemic worldwide and is the key to constructing a line of defense against the virus, while the epidemic prevention policies adopted by governments have an important impact on the protective behavior of the public. The purpose of this study is to analyze the mechanism by which the stringency of epidemic prevention policies affects public vaccination willingness and to explore the mediating effect of public risk perception.MethodsA cross-sectional survey was conducted among 387 adults from December 2022 to June 2023 in China. A multiple linear regression model was used to explore the impact of epidemic prevention policy stringency on public vaccination willingness, and a hierarchical regression model was used to test the mediating effect of public risk perception.ResultsOur results showed that public vaccination willingness increased by approximately 45.5% for every one-unit increase in the stringency of the epidemic prevention policies, which shows that the stringency of epidemic prevention policies has a significant positive influence on public vaccination willingness. In addition, public risk perception increased by approximately 38.9% during the period of stringent government policies on epidemic prevention. For every one-unit increase in risk perception, public vaccination willingness increased by approximately 40.9%, and the relationship between the stringency of epidemic prevention policies and public vaccination willingness was partially mediated by risk perception.ConclusionThe stricter the epidemic prevention policies, the stronger the public vaccination willingness; risk perception plays a mediating effect between the stringency of epidemic prevention policies and public vaccination willingness. This finding is particularly important for exploring and analyzing the factors influencing public vaccination willingness and for improving public health.

show abstract

Measuring the impact of the COVID-19 epidemic on university resumption and suggestions for countermeasures

Cited by 6 publications

References 4 publications

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

The Impact of COVID-19 Risk Perception on College Students' Positive Psychological Capital: the Role of Psychological Resilience and Social Support

Impact of epidemic prevention policies on public vaccination willingness: empirical research in China

Contact Info

Product

Resources

About