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

Abstract

The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and the trend of confirmed cases of infection, while paying particular attention to the sickness level. The study is carried out in relation to the Italian case, but the result is of more general importance, particularly for countries with limited ICU (intensive care units) availability. The statistical analysis showed that, by increasing the number of tests, the trend of home isolation cases was positive. However, the trend of mild cases admitted to hospitals, intensive case cases, and daily deaths were all negative. The result of the statistical analysis provided the basis for an AI study by ANN. In addition, the results were validated using a multivariate linear regression (MLR) approach. Our main result was to identify a significant statistical effect of a reduction of pressure on the health care system due to an increase in tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality diseases, such as, e.g., cardiological and oncological ones. Our results show that swab testing may play a significant role in decreasing stress on the health system. Therefore, this case study is relevant, in particular, for plans to control the pandemic in countries with a limited capacity for admissions to ICU units.

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

“…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 ).…”

“…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 ).…”

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

“…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).…”

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

“…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.…”