جستجوی مقالات مرتبط با کلیدواژه « Statistical modeling » در نشریات گروه « پزشکی »

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.

The COVID-19 pandemic has had a significant impact on the well-being of different individuals and nations in terms of socioeconomic, psychological, and public health issues. Regarding the importance of predicting the peak outbreaks and incidence of COVID-19, this study aimed to predict factors that affect the incidence of COVID-19 pandemic in hospitalized patients admitted to a hospital in Khorramabad city of Iran, using a multivariate logistic regression model.

In this descriptive-analytical study, the data from 4,425 COVID-19 patients in the first peak of the disease, who were referred to the Shohada-ye Ashayer Hospital, Khorramabad, Iran, in 2020, were examined, from whom 2,978 people had undergone COVID-19 test. Data were collected using a researcher-made checklist for variables and examination of patients’ daily records. Data analysis was performed using descriptive methods, multivariate logistic regression model, and backup vector machine method in the R software for the modeling of risk factors associated with the disease diagnosis.

The highest and lowest rates of incidence were observed in the age range of 40-49 (19%) and 19-10 years (1%).  The age group 60-69 years accounted for 6% of the population with a mortality rate of 25%. Based on the implemented model, the most effective symptoms associated with the incidence of COVID-19 included age, fever, decreased level of consciousness, blood oxygen level, and a history of heart disease.

The present study showed that identification of the effective variables of the disease led to the identification of high-risk individuals. This method can be used to prevent disease incidence and prevalence in high-risk groups and is a great help in controlling the COVID-19 pandemic.