COVID-19's daily increasing cases and deaths have led to worldwide lockdown, quarantine and some restrictions. This study aims to analyze the effect of lockdown days on the spread of coronavirus in countries. COVID-19 cases and lockdown days data were collected for 49 countries that implemented the lockdown between certain dates (without interruption). The correlation tests were used for data analysis based on unconstrained (normal) and constrained (Tukey-lambda). The lockdown days was significantly correlated with COVID-19 pandemic based on unconstrained (r = −0.9126, F-ratio = 6.1654; t-ratio = 2.40; prob > .0203 with 49 observations) and based on Tukey-lambda (r = 0.7402, λ = 0.14). The lockdown, one of the social isolation restrictions, has been observed to prevent the COVID-19 pandemic, and showed that the spread of the virus can be significantly reduced by this preventive restriction in this study. This study offers initial evidence that the COVID-19 pandemic can be suppressed by a lockdown. The application of lockdown by governments is also thought to be effective on psychology, environment and economy besides having impact on Covid-19.

A healthcare resource allocation generally plays a vital role in the number of patients treated (p_nt) and the patient waiting time (w_t) in healthcare institutions. This study aimed to estimate p_nt and w_t as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δ_i) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δ_i of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p_nt; 0.9514, 0.9517, 0.9514, and 0.9514 for w_t, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ_0.2 (AB had a better accuracy at 0.8709 based on the value of δ_0.2 for p_nt) in the test stage. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p_nt; 0.8820, 0.8821, 0.8819, and 0.8818 for w_t in the training phase, respectively. All scenarios created by the δ_i coefficient should be preferred for ED since the income provided by the p_nt value to the hospital was more than the cost of healthcare resources. On the contrary, the w_t estimation results of ML algorithms based on the δ_i coefficient differed. Although w_t values in all ML algorithms with δ_0.0 and δ_0.1 coefficients reduced the cost of the hospital, w_t values based on δ_0.2 and δ_0.3 increased the cost of the hospital.

Goal: this study aimed to use the experimental simulation method to obtain the optimum results by using the simulation technique of the models, which are modeled mathematically but not solved. Design/Methodology/Approach: a case study was conducted to apply the discrete-event simulation (DES) approach based on the design of experiment (DOE) technique to derive strategies and analyze situations in an emergency department (ED). Results: by integrating the DOE approach with the DES technique for the ED, the average waiting time for a patient in a small ED was reduced from 40.09 minutes to 9.83 minutes (75.48%), which is a significant result. Increasing the capacity of the resources without increasing the number of daily resources for the ED resulted in an increase from 133 to 147 (10.53%) in the number of patients treated. Additionally, the optimum number of healthcare resources to employ in EDs was calculated. Limitations: there is no shift system for healthcare resources in the DES model. Since the ED was considered in this study, the same number of resources was run during the working hours of the DES model. Practical implications: for researchers and healthcare professionals, the developed method is used to obtain results that cannot be obtained through the mathematical modeling of healthcare problems. The results of the changes in health resources on the outputs are obtained by this method as the shortest time frame and lowest cost. Originality/Value: there is almost no detailed study of DES with the DOE technique. Providing high reliability has resulted in obtaining optimum values through the integration of the DOE approach with the DES technique in the healthcare area. It is very difficult to get accurate results anywhere where the human factor is present.