محمدحسین کریمی زارچی

Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases.

Coronavirus disease 2019 is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to modeling, comparing the performance of models, and predict new cases and deaths efficiently in the future.

In this article nine popular forecasting techniques are tested on the data of Covid-19 in Bahabad city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. 

The results of the analysis showed that the best model according to the evaluation criteria for forecasting cumulative cases of hospitalization of Covid-19 is the cubic spline smoothing model, and cumulative cases of death, KNN regression model. Also, autoregressive neural network and theta models for hospitalization cases, and for death cases, autoregressive neural network model has the worst performance among other models.

This study can provide a proper understanding of the spread of covid-19 disease in this region so that by taking precautionary measures and formulating appropriate policies, this epidemic can be effectively overcome. Also, unlike other studies, this study uses 9 different techniques and their comparison, which in turn increases the confidence factor in decision making. Also, an important point is that the data should be updated in real time.

Coronavirus disease 2019 or COVID-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths today can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future.

In this article, 9 forecasting techniques were tested on the data of COVID-19 of Mehriz city, Iran as a case study from 2020/02/26 to 2021/12/19 and using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models were compared.

For cumulative cases of hospitalization, ARIMA, Exponential, Holt-Winters, and STL models performed better and autoregressive neural networks, Theta, and KNN regression showed poor performance. Also, for cumulative mortality cases, KNN regression, Exponential and Theta models have better performance in predicting cumulative mortality cases, and autoregressive neural networks, ARIMA, and cubic spline smoothing showed poor performance.

the best model according to the mentioned evaluation criteria for predicting cumulative cases of hospitalization of COVID-19 is STL model and for cumulative cases of death is the KNN regression model. Also, the autoregressive neural network model has the worst performance among other models, both for hospitalization and death cases. Also, the important point is that the data should be updated in real-time.

Smoking contributes to more than 5million deaths in the world annually. More than 70% of these deaths occurs in the developing and 30% in the developed countries. the important way to stop these deaths, is to quit smoking. Despite the perceived risk, more than 40% of smokers that had heart attack, began smoking after leaving the hospital. This indicates that inadequacy of awareness in them. The aim of this study was to study Routine or verbal-illustrative Education on nicotine dependency in men after Coronary Artery Bypass Graft (CABG) This was a clinical trials study,conducted on 100 patients that were candidates for heart surgery at Afshar hospital in Yazd. The participants were randomly divided into two groups. The data were collected by two questionnaires before and after the heart surgery. The first questionnaire included questions about demographic variables, such as date of surgery, address, telephone number, standard test of nicotine dependence (Fagerstrom) and standard test of self – efficacy (Prochaska- DiClemente). After collecting the data at the begining, the case group was treated on routine and verbal education of quit smoking by three pictures about clogging of arteries in 5 minutes. The control group was treated with the routine education. Then the questionnaires were completed 3 months after surgery. The questions of these questionnaires were about quitting smoking, and date of the onset of smoking after surgery. All the registered data were transferred into SPSS software and analyzed. Before intervention the two the groups were same in terms of demographic variables. The mean ages the of case and control groups were 53.55±8.9 and 55.6±8.4 respectively. Their mean age of beginning with smoking was 21.6±6. The extent of nicotine in dependency in the case group was 5.25 and in the control groups 4.86. after 3 months of surgery the extent of quit smoking in case group turned out to be 79.6% and in control groups 66%. There was no significant difference between two groups in quitting smoking. The data indicated that the effect of education through showing picture is more than the routine education.