The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

Nejad, Hana Javadi; Violini, G.; Pirouz, Behrouz

doi:10.3390/info11090454

Cited by 10 publications

(19 citation statements)

References 56 publications

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“…To sum up, this research illustrates that it is possible to construct a model based on the ANN to predict data variables presented before and after COVID-19 ( Pirouz et al, 2020 ), and more specifically, in the educational field, where studies involving ANNs are scarce ( Martínez and Morales, 2020 ).…”

Section: Discussionmentioning

confidence: 97%

“…In addition to this, artificial intelligence has been used to examine multiple variables that define the pandemic and to combat COVID-19 ( Haghshenas et al, 2020 ; Pirouz et al, 2020 ). Predictive models have been postulated as effective tools to meet the psychological needs, although its use is more widespread to other very diverse fields such as economics, medicine and transportation ( Wu and Feng, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment

et al. 2021

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The COVID-19 global health emergency has greatly impacted the educational field. Faced with unprecedented stress situations, professors, students, and families have employed various coping and resilience strategies throughout the confinement period. High and persistent stress levels are associated with other pathologies; hence, their detection and prevention are needed. Consequently, this study aimed to design a predictive model of stress in the educational field based on artificial intelligence that included certain sociodemographic variables, coping strategies, and resilience capacity, and to study the relationship between them. The non-probabilistic snowball sampling method was used, involving 337 people (73% women) from the university education community in south-eastern Spain. The Perceived Stress Scale, Stress Management Questionnaire, and Brief Resilience Scale were administered. The Statistical Package for the Social Sciences (version 24) was used to design the architecture of artificial neural networks. The results found that stress levels could be predicted by the synaptic weights of coping strategies and timing of the epidemic (before and after the implementation of isolation measures), with a predictive capacity of over 80% found in the neural network model. Additionally, direct and significant associations were identified between the use of certain coping strategies, stress levels, and resilience. The conclusions of this research are essential for effective stress detection, and therefore, early intervention in the field of educational psychology, by discussing the influence of resilience or lack thereof on the prediction of stress levels. Identifying the variables that maintain a greater predictive power in stress levels is an effective strategy to design more adjusted prevention programs and to anticipate the needs of the community.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment

et al. 2021

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“…This model just predicts using two parameters. With the development of linear regression, MLR can be used to expect more than two parameters [44], [45]. The MLR model can be shown in (1).…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Comparative analysis of time series prediction model for forecasting COVID-19 trend

Wahyuni

Sediyono

Sembiring

et al. 2022

IJEECS

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<span>The outbreak of the COVID-19 pandemic occurred some time ago, making the world a pandemic. Based on this condition is important to predict early to prevent the COVID-19 disease if someday pandemic occurs. The aim of the study is to compare the analysis result of cumulative cases of COVID-19 using multiple linear regression (MLR), ridge regression (RR), and <a name="_Hlk111113162"></a>long short term memory (LSTM) models for cases study Java and Bali islands. We chose both islands as a case study because they have very dense populations. These three models are the most widely used time series-based prediction models and have relatively high accuracy values. The predictive variables used are the number of cumulative cases, the daily cases, and population density. The research data was taken from Kaggle and processed using google collabs. Data was taken from January 20, 2020, to August 8, 2020, and data training was carried out for 12 days. The results show the accuracy of LSTM is better than other models. it can be seen in the accuracy value (99.8 %) of the model test result. The testing model uses R2, mean square error (MSE), and root mean square error (RMSE).</span>

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“…Determining the optimal number of students to test is critical in mitigating any spread of the virus on a campus, as also seen in [85] , where an optimization model was developed to estimate the optimal number. In terms of utilizing testing to determine mitigation strategies, [88] found a parallel relationship between increasing the number of tests with increasing numbers of those isolated in Italy. In a different study [89] , also involving Italy, an optimization model was constructed and solved to determine how much diagnostic testing would need to be conducted per region in order to maximize COVID-19 infection detection capabilities.…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

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This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

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The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

Cited by 10 publications

References 56 publications

Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment

Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment

Comparative analysis of time series prediction model for forecasting COVID-19 trend

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

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