Background: The outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease. For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran. Methods: The information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29th Apr. Artificial Neural Networks (ANN) and Auto-Regressive Integrated Moving Average (ARIMA) models were applied for prediction. The data was prepared from daily reports of Iran Ministry of Health and open datasets provided by the JOHN Hopkins. To compare models, dataset was separated into train and test sets. Mean Squared Error (MSE) and Mean Absolute Error (MAE) was the comparison criteria. Results: Both algorithms forecasted an exponential increase in number of newly infected patients. If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29th Apr, respectively by ANN and ARIMA. While Model comparison confirmed that ARIMA prediction was more accurate than ANN. Conclusion: COVID-19 is contagious disease, and has infected many people in Iran. Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed.

Background: Coronavirus, the cause of severe acute respiratory syndrome (COVID-19), is rapidly spreading around the world. Since the number of corona positive patients is increasing sharply in Iran, this study aimed to forecast the number of newly infected patients in the coming days in Iran. Methods: The data used in this study were obtained from daily reports of the Iranian Ministry of Health and the datasets provided by the Johns Hopkins University including the number of new infected cases from February 19, 2020 to March 21, 2020. The autoregressive integrated moving average (ARIMA) model was applied to predict the number of patients during the next thirty days. Results: The ARIMA model forecasted an exponential increase in the number of newly detected patients. The result of this study also show that if the spreading pattern continues the same as before, the number of daily new cases would be 3574 by April 20. Conclusion: Since this disease is highly contagious, health politicians need to make decisions to prevent its spread; otherwise, even the most advanced and capable health care systems would face problems for treating all infected patients and a substantial number of deaths will become inevitable.

Background The prevalence of cardiovascular disease (CVD) is rapidly increasing in the world. The present study aimed to assess the prevalence and Predictors factors of CVD based on the data of Kherameh cohort study. Methods The present cross-sectional, analytical study was done based on the data of Kherameh cohort study, as a branch of the Prospective Epidemiological Studies in Iran (PERSIAN). The participants consisted of 10,663 people aged 40–70 years. CVD was defined as suffering from ischemic heart diseases including heart failure, angina, and myocardial infarction. Logistic regression was used to model and predict the factors related to CVD. Additionally, the age-standardized prevalence rate (ASPR) of CVD was determined using the standard Asian population. Results The ASPR of CVD was 10.39% in males (95% CI 10.2–10.6%) and 10.21% in females (95% CI 9.9–10.4%). The prevalence of CVD was higher among the individuals with high blood pressure (58.3%, p < 0.001) as well as among those who smoked (28.3%, p = 0.018), used opium (18.2%, p = 0.039), had high triglyceride levels (31.6%, p = 0.011), were overweight and obese (66.2%, p < 0.001), were unmarried (83.9%, p < 0.001), were illiterate (64.2%, p < 0.001), were unemployed (60.9%, p < 0.001), and suffered from diabetes mellitus (28.1%, p < 0.001). The results of multivariable logistic regression analysis showed that the odds of having CVD was 2.25 times higher among the individuals aged 50–60 years compared to those aged 40–50 years, 1.66 folds higher in opium users than in non-opium users, 1.37 times higher in smokers compared to non-smokers, 2.03 folds higher in regular users of sleeping pills than in non-consumers, and 4.02 times higher in hypertensive individuals than in normotensive ones. Conclusion The prevalence of CVD was found to be relatively higher in Kherameh (southern Iran) compared to other places. Moreover, old age, obesity, taking sleeping pills, hypertension, drug use, and chronic obstructive pulmonary disease had the highest odds ratios of CVD.

Background: Today, the prevalence of Internet Addiction (IA) is increasing among college students and the mental health of students is reduced with the increasing severity of IA. Objectives: The aim of this study was to evaluate IA and mental health among medical sciences students in the southeast of Iran. Materials and Methods: This cross-sectional study was carried out on 417 students of Zahedan University of Medical Sciences, Zahedan, Southeast of Iran, during year 2016. The participants were recruited through a two-stage stratified sampling method. The data collection was done using Young Internet Addiction Test (YIAT) and Goldberg General Health (GHQ) standard questionnaires. Data analysis were performed by ANOVA, Pearson correlation, chi-square, and logistic regression tests using SPSS software for Windows version 16. Results: The overall prevalence of IA in students was 27.56% (95% CI: 21.3 to 30.8). The prevalence of IA was 15.82% (95% CI: 11.3 to

Objectives and background People with diabetes (PWD) are one of the high-risk groups for coronavirus disease 2019 (COVID-19) infection, increasing the disease mortality. This study was aimed to compare the epidemiological characteristics and outcomes of COVID-19 in diabetic versus non-diabetic individuals. Methods In this retrospective observational study, the epidemiological characteristics of the two groups of diabetic ( n =1365) and non-diabetic ( n =15,026) subjects with definite diagnosis of COVID-19 in the southwestern region of Iran were compared. All clinical signs and comorbidities of the patients were evaluated. Chi-square test was used to examine the differences in qualitative variables between diabetic and non-diabetic groups. Results Of 16,391 enrolled subjects, 8.3% had diabetes, and 28.3% of COVID-19-related deaths occurred in diabetics. Also, the mortality rate among diabetics was reported as 14.3%. The average age of diabetic patients and non-diabetic patients was 59 and 37 years, respectively. The odds of fever, cough, shortness of breath, headache, and underlying diseases, such as hypertension, cardiovascular disease, chronic lung disease, immune deficiency, and hyperlipidemia, were significantly higher in diabetic patients than in non-diabetics. Conclusion Diabetes is associated with increased mortality rate in patients with COVID-19 and is considered as a major risk factor for COVID-19 infection, posing a major public health challenge for health policymakers in managing and controlling the disease. Therefore, development of prevention and treatment strategies aimed at reducing COVID-19 morbidity and mortality in diabetes patients is of significant importance.