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.

Introduction: The prevalence of type 2 diabetes mellitus (T2D) has increased considerably while the outcome of diabetic management is suboptimal. In order to promote diabetic management, associated factors for adherence to medications, diet, and physical activity (PA) need to be more clearly identified amongst patients with T2D. Methods: This cross-sectional study was conducted on 206 men and 294 women amongst patients with diabetes who were registered in ten special diabetic clinics in Shiraz, Iran from November 2018 to April 2019. Levels of adherence to medication, Mediterranean diet (MD), and physical activity were measured with validated and appropriate questionnaires. Results: Mean age (± SD) was 56.92 ± 0.52 years and 294 (58.8%) were female. Data showed that reduced adherence to medication, MD, and PA increased glycated hemoblobin (HbA1c). Adherence to medication among low, moderate, and high levels was 27.2%, 59.2%, and 13.6%, respectively. Associated factors such as 50-64 years of age, at least 65 years of age, overweight, obese, divorced widow, smoker, and exsmoker had a significant influence on adherence to medication, P = 0.017, P = 0.018, P = 0.008. P = 0.045, P = 0.026, P\0.004, and P = 0.001, respectively. In addition, adherence to MD among low, moderate, and high levels was 5.4%, 77.2%, and 17.4%, respectively. Associated factors such as 50-64 years of age, at least 65 years of age, overweight, obese, and smoker had a significant influence on adherence to MD, P = 0.011, P = 0.046, P = 0.002, P\0.001, and P = 0.032, respectively. Furthermore, adherence to PA among low, moderate, and high levels was 21%, 68.6%, and 10.4%, respectively. Only the non-smoker factor played a significant role in PA adherence, P = 0.010. Conclusion: Levels of adherence to medications, diet, and PA amongst patients with T2D are not within the acceptable range. Recognized associated factors that contribute to adherence might improve diabetes management and allow one to tailor the appropriate message to improve glycemic indices.Enhanced Digital Features To view enhanced digital features for this article go to https://doi.org/10.6084/ m9.figshare.11358593. A. Mirahmadizadeh Key Summary PointsWhy carry out this study?In order to promote diabetic management, the associated factors for adherence to medications, diet, and physical activity (PA) need to be more clearly identified amongst patients with type 2 diabetes (T2D).Although the levels of adherence to medication, diet, and physical activity have been evaluated separately in other studies in patients with T2D, few studies have simultaneously evaluated the adherence to these factors.What was learned from the study?Reduced adherence to medication, Mediterranean diet (MD), and PA increased HbA1c.Adherence to medication among low, moderate, and high levels was 27.2%, 59.2%, and 13.6%, respectively. Associated factors such as 50-64 years of age, at least 65 years of age, overweight, obese, divorced widow, smoker, and exsmoker had a significant influence on adherence to medication...

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The Framingham 10-year cardiovascular disease risk is measured by laboratory-based and non-laboratory-based models. This study aimed to determine the agreement between these two models in a large population in Southern Iran. In this study, the baseline data of 8138 individuals participated in the Pars cohort study were used. The participants had no history of cardiovascular disease or stroke. For the laboratory-based risk model, scores were determined based on age, sex, current smoking, diabetes, systolic blood pressure (SBP) and treatment status, total cholesterol, and High-Density Lipoprotein. For the non-laboratory-based risk model, scores were determined based on age, sex, current smoking, diabetes, SBP and treatment status, and Body Mass Index. The agreement between these two models was determined by Bland Altman plots for agreement between the scores and kappa statistic for agreement across the risk groups. Bland Altman plots showed that the limits of agreement were reasonable for females < 60 years old (95% CI: −2.27–4.61%), but of concern for those ≥ 60 years old (95% CI: −3.45–9.67%), males < 60 years old (95% CI: −2.05–8.91%), and males ≥ 60 years old (95% CI: −3.01–15.23%). The limits of agreement were wider for males ≥ 60 years old in comparison to other age groups. According to the risk groups, the agreement was better in females than in males, which was moderate for females < 60 years old (kappa = 0.57) and those ≥ 60 years old (kappa = 0.51). The agreement was fair for the males < 60 years old (kappa = 0.39) and slight for those ≥ 60 years old (Kappa = 0.14). The results showed that in overall participants, the agreement between the two risk scores was moderate according to risk grouping. Therefore, our results suggest that the non-laboratory-based risk model can be used in resource-limited settings where individuals cannot afford laboratory tests and extensive laboratories are not available.

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.