Interval Type-2 Fuzzy Control and Stochastic Modeling of COVID-19 Spread Based on Vaccination and Social Distancing Rates

Background and objective : Besides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions. Methods : Here, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate. Results : The results show the robustness and efficiency of the proposed method in the presence of up to 3% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario. Conclusions : The proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.

Interval type-2 Fuzzy control and stochastic modeling of COVID-19 spread based on vaccination and social distancing rates.

BACKGROUND AND OBJECTIVE: Besides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions. METHODS: Here, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate. RESULTS: The results show the robustness and efficiency of the proposed method in the presence of up to 1% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario. CONCLUSIONS: The proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.

Intensive Care Unit, Cardiac Care Unit, and Emergency Department Nurses? Perceptions of Medical Device-Related Pressure Injuries: A Cross-Sectional Study br

BACKGROUND: Nurses' perception of medical device-related pressure injuries (MDRPIs) may affect their performance, but there is a lack of studies in this area.PURPOSE: The current study aimed to examine intensive care unit (ICU), cardiac care unit (CCU) and emergency department nurses' perception of proper prevention of MDRPIs and care for individuals with such injuries.METHODS: This descriptive study was conducted in 4 general hospitals in Iran in 2021. All nurses (N = 310) working in ICUs, CCUs and emergency departments of these facilities were invited to complete a researcher-made demographic check-list and an 11-item questionnaire to assess attitudes toward MDRPIs. The questionnaire item responses were scored from 1 (strongly agree) to 5 (strongly disagree) with the total score for the 11 items ranging from 11 to 55. A score of 11 to 25 was categorized as indicating a negative attitude toward proper prevention of MDRPIs and care for such patients;a score of 26 to 40 indicated a neutral attitude, and a score >40 indicated a positive attitude.RESULTS: A total of 260 nurses fulfilled the data col-lection tool. The response rate was 83.8%. The mean total score of attitude toward MDRPIs was 41.7. No significant relationship was observed between the total score of nurses' attitudes and their demographic variables. Of the 260 participants, 159 stated they had not received any trainings on MDRPIs at nursing schools during their education, 212 stated they had not participated in any scientific workshops on MDRPIs, and 167 described their knowledge about the prevention and care of MDRPIs as insuf-ficient.CONCLUSION: Among ICU, CCU, and emergency nurses in Iran, most had a positive attitude toward the prevention and care of MDRPIs, but steps should be taken to offer more opportunities for nurses to increase their knowledge in this area