Exploring the potential of learning methods and recurrent dynamic model with vaccination: A comparative case study of COVID-19 in Austria, Brazil, and China.

In order to effectively manage infectious diseases, it is crucial to understand the interplay between disease dynamics and human conduct. Various factors can impact the control of an epidemic, including social interventions, adherence to health protocols, mask-wearing, and vaccination. This article presents the development of an innovative hybrid model, known as the Combined Dynamic-Learning Model, that integrates classical recurrent dynamic models with four different learning methods. The model is composed of two approaches: The first approach introduces a traditional dynamic model that focuses on analyzing the impact of vaccination on the occurrence of an epidemic, and the second approach employs various learning methods to forecast the potential outcomes of an epidemic. Furthermore, our numerical results offer an interesting comparison between the traditional approach and modern learning techniques. Our classic dynamic model is a compartmental model that aims to analyze and forecast the diffusion of epidemics. The model we propose has a recurrent structure with piecewise constant parameters and includes compartments for susceptible, exposed, vaccinated, infected, and recovered individuals. This model can accurately mirror the dynamics of infectious diseases, which enables us to evaluate the impact of restrictive measures on the spread of diseases. We conduct a comprehensive dynamic analysis of our model. Additionally, we suggest an optimal numerical design to determine the parameters of the system. Also, we use regression tree learning, bidirectional long short-term memory, gated recurrent unit, and a combined deep learning method for training and evaluation of an epidemic. In the final section of our paper, we apply these methods to recently published data on COVID-19 in Austria, Brazil, and China from 26 February 2021 to 4 August 2021, which is when vaccination efforts began. To evaluate the numerical results, we utilized various metrics such as RMSE and R-squared. Our findings suggest that the dynamic model is ideal for long-term analysis, data fitting, and identifying parameters that impact epidemics. However, it is not as effective as the supervised learning method for making long-term forecasts. On the other hand, supervised learning techniques, compared to dynamic models, are more effective for predicting the spread of diseases, but not for analyzing the behavior of epidemics.

Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19.

It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system's parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.

Effect of Sleep Intervention Programs during Cardiac Rehabilitation on the Sleep Quality of Heart Patients.

Materials and Methods: In this quasi-experimental study with unequal control group design, 35 individuals participated in the cardiac rehabilitation program as the experimental group and 35 served as the control group. The program included 12 weeks of exercise, 3 sessions per week, 3 sessions of training programs each lasting for 45 minutes, and a special two-session sleep improvement program. Data were collected using the Pittsburgh Sleep Quality Index and analysed with descriptive and inferential statistical methods. Results: There were not any significant differences between the two groups in age, sex, marital status, smoking, and indication for cardiac rehabilitation (P > 0.05). The scores of sleep quality of patients were 9.2 ± 1.58 before and 4.40 ± 1.14 after intervention in the experimental group and 9.02 ± 2.56 before and 7.48 ± 1.86 after intervention in the control group. There was no significant difference between the two groups before intervention (P = 0.73). yet there was a significant difference after intervention (P = 0.0001). In addition, scores of sleep quality of patients were significantly different in the experimental and control groups before and after intervention (P = 0.0001). Conclusion: Findings indicated that the quality of sleep of cardiac patients improved after the sleep intervention program during the cardiac rehabilitation program. Therefore, it is suggested to implement sleep improvement programs for cardiac patient care as an effective, easy, and feasible technique. In addition, it is necessary to pay more attention to the sleep improvement program in cardiac rehabilitation. Trial Registration. The trial was retrospectively registered on https://en.irct.ir/trial/50799 on 14 September 2020 (14.09.2020) with registration number IRCT20140307016870N6.

Diagnostic value of ultrasonography in spinal abnormalities among children with neurogenic bladder.

BACKGROUND: Nowadays, magnetic resonance imaging (MRI) is the gold standard for evaluation and diagnosis of spinal cord abnormalities, which are considered among the leading causes of neurogenic bladder; however, MRI is a costly imaging method and is not available at all health centers. Sporadic studies have shown the alignment of MRI with ultrasonography results in diagnosis of spinal abnormalities; although none of these studies has expressed the diagnostic value of ultrasonography. OBJECTIVE: The aim of this study was to evaluate the diagnostic value of ultrasonography in detection of spinal abnormalities in children with neurogenic bladder. METHODS: This is a cross-sectional study carried out from January 2014 to November 2015 on patients with neurogenic bladder referred to Department of Radiology, Dr. Sheikh Hospital, Mashhad University of Medical Sciences, Mashhad, Iran. All patients underwent sonography of the spinal cord and soft-tissue masses; also, a spinal MRI scan was performed. The existence of spina bifida, sacral agenesis, posterior vertebral arch defects, mass, tethered cord, myelomeningocele, lipoma and fatty infiltration, dural ectasia, hydromyelia and syringomyelia, and diastomatomyelia was recorded during each imaging scan. Chi-square and Fisher's tests were used for data analysis using SPSS 19.0 software, and the sensitivity and specificity of ultrasonography findings were calculated by MedCale 26 software. RESULTS: Forty patients with neurogenic bladder (22 males/18 females), with an average of 25.73±19.15 months, were enrolled. The most common abnormality was found in patients' MRI was tethered cord syndrome (70%). There was a significant relationship between ultrasonographic and MRI findings in spina bifida abnormalities (p=0.016), sacral agenesis (p=0.00), tethered cord (p=0.00), myelomeningocele (p=0.00), and lipoma and fatty infiltration (p=0.01). Ultrasonography had a sensitivity of 20.0%-100% and a specificity of 85.7%-100% depending on the detected type of abnormality. CONCLUSION: It seems that ultrasonography has an acceptable and desirable sensitivity and specificity in the diagnosis of most of the spinal cord abnormalities in children with a neurogenic bladder.

Comparison Between Diuretic Urography (IVP) and Diuretic Renography for Diagnosis of Ureteropelvic Junction Obstruction in Children.

BACKGROUND: Ureteropelvic junction obstruction (UPJO) is one of the most common causes of urinary tract obstruction in children. Several methods are used to diagnose upper urinary tract obstruction including renal ultrasonography (US), intravenous pyelogram (IVP), diuretic renography (DR), magnetic resonance urography (MRU) and antegrade or retrograde pyelography. Nowadays it is suggested to use diuretic renography as the best method for diagnosing of UPJO. There is no comparative study between IVP and DR scan for diagnosis of UPJO in children. OBJECTIVES: The aim of the present study was to compare IVP with furosemide injection and diuretic renography in diagnosis of clinically significant UPJO. PATIENTS AND METHODS: This was a cross sectional study performed in 153 UPJO suspected children (121 boys, 32 girls) based on US findings in cases presented with urinary tract infection (UTI), prenatal hydronephrosis, abdominal/flank pain, abdominal mass and hematuria. Renal ultrasound was used as an initial screening tool for detection of urinary tract abnormality. Vesicoureteral reflux (VUR) was ruled out by voiding cystourethrography (VCUG). Serum creatinin, blood urea nitrogen, urinalysis and urine culture was screened in all cases. IVP with furosemide and DR were performed as soon as possible after the mentioned workup. RESULTS: During a five year period, 46 out of 153 patients were diagnosed as UPJO based on diuretic renography: the age ranged from 4 months to 13 years (mean: 3.1 ± 0.78 years). There was a significant higher (76%) proportion of UPJO in the boys and in the left side (78%). The sensitivity of IVP with furosemide injection in diagnosis of UPJO was 91.3% whereas DR was accepted as standard for diagnostic procedure in diagnosis of UPJO. CONCLUSIONS: Although DR is accepted as the best method for diagnosis of UPJO, we found a small sensitivity difference between IVP and DR in kidneys with normal or near normal function. In many settings such as small cities lacking facilities for advanced isotope imaging technology, use of IVP with diuretic maybe an acceptable procedure for diagnosis of UPJO.

Comparison of combined treatment with desmopressin plus oxybutynin and desmopressin plus tolterodine in treatment of children with primary nocturnal enuresis.

INTRODUCTION: Nocturnal enuresis (enuresis) is one of the most common developmental problems of childhood, which has often a familial basis, causes mental and psychological damage to the child and disrupts family solace. OBJECTIVES: In this study, we compared therapeutic effects of combination therapy of desmopressin plus oxybutynin with desmopressin plus tolterodine, in the treatment of children with primary nocturnal enuresis. PATIENTS AND METHODS: The present study is a clinical trial study, where 59 patients with primary nocturnal enuresis in the age range of 5 to 14 years old were selected from the visitors of nephrology clinic of Dr. Sheikh pediatrics hospital (Mashhad, Iran). Patients were divided into 2 treatment groups where the first group received combined therapy with desmopressin and oxybutynin, and the second group received combined therapy with desmopressin and tolterodine. Data was analyzed using SPSS 16 software and descriptive and analytical statistics (chi-square test). RESULTS: The mean of age of patients in total was 2.55 ± 7.90 years. In the treatment group with desmopressin and oxybutynin, 26 of 30 patients (86.7%) achieved a complete remission and 4 patients (13.3%) still suffered from enuresis during a 3-month evaluation. The comparison of 2 groups, in terms of the outcome of the 3-month treatment, showed significant differences between the remission and recovery of 2 groups, where the recovery in the group with desmopressin plus tolterodine was higher than the group with desmopressin plus oxybutynin (P = 0.001). CONCLUSION: The results showed that combined treatment with desmopressin plus tolterodine performs better than desmopressin plus oxybutynin .