On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events.

The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan's daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model.

Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine leaning techniques.

Statistical modeling and forecasting of time-to-events data are crucial in every applied sector. For the modeling and forecasting of such data sets, several statistical methods have been introduced and implemented. This paper has two aims, i.e., (i) statistical modeling and (ii) forecasting. For modeling time-to-events data, we introduce a new statistical model by combining the flexible Weibull model with the Z-family approach. The new model is called the Z flexible Weibull extension (Z-FWE) model, where the characterizations of the Z-FWE model are obtained. The maximum likelihood estimators of the Z-FWE distribution are obtained. The evaluation of the estimators of the Z-FWE model is assessed in a simulation study. The Z-FWE distribution is applied to analyze the mortality rate of COVID-19 patients. Finally, for forecasting the COVID-19 data set, we use machine learning (ML) techniques i.e., artificial neural network (ANN) and group method of data handling (GMDH) with the autoregressive integrated moving average model (ARIMA). Based on our findings, it is observed that ML techniques are more robust in terms of forecasting than the ARIMA model.

On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data.

Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.

A new Cosine-Weibull model: Distributional properties with applications to basketball and medical sectors

The two-parameter classical Weibull distribution is commonly implemented to cater for the product’s reliability, model the failure rates, analyze lifetime phenomena, etc. In this work, we study a novel version of the Weibull model for analyzing real-life events in the sports and medical sectors. The newly derived version of the Weibull model, namely, a new cosine-Weibull (NC-Weibull) distribution. The importance of this research is that it suggests a novel version of the Weibull model without adding any additional parameters. Different distributional properties of the NC-Weibull distribution are obtained. The maximum likelihood approach is implemented to estimate the parameters of the NC-Weibull distribution. Finally, three applications are analyzed to prove the superiority of the NC-Weibull distribution over some other existing probability models considered in this study. The first and second applications, respectively, show the mortality rates of COVID-19 patients in Italy and Canada. Whereas, the third data set represents the injury rates of the basketball players collected during the 2008–2009 and 2018–2019 national basketball association seasons. Based on four selection criteria, it is observed that the NC-Weibull distribution may be a more suitable model for considering the sports and healthcare data sets.

On Predictive Modeling Using a New Flexible Weibull Distribution and Machine Learning Approach: Analyzing the COVID-19 Data

Predicting and modeling time-to-events data is a crucial and interesting research area. For modeling and predicting such types of data, numerous statistical models have been suggested and implemented. This study introduces a new statistical model, namely, a new modified flexible Weibull extension (NMFWE) distribution for modeling the mortality rate of COVID-19 patients. The introduced model is obtained by modifying the flexible Weibull extension model. The maximum likelihood estimators of the NMFWE model are obtained. The evaluation of the estimators of the NMFWE model is assessed in a simulation study. The flexibility and applicability of the NMFWE model are established by taking two datasets representing the mortality rates of COVID-19-infected persons in Mexico and Canada. For predictive modeling, we consider two pure statistical models and two machine learning (ML) algorithms. The pure statistical models include the autoregressive moving average (ARMA) and non-parametric autoregressive moving average (NP-ARMA), and the ML algorithms include neural network autoregression (NNAR) and support vector regression (SVR). To evaluate their forecasting performance, three standard measures of accuracy, namely, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are calculated. The findings demonstrate that ML algorithms are very effective at predicting the mortality rate data.

A global report on the dynamics of COVID-19 with quarantine and hospitalization: A fractional order model with non-local kernel.

In this paper, a compartmental mathematical model has been utilized to gain a better insight about the future dynamics of COVID-19. The total human population is divided into eight various compartments including susceptible, exposed, pre-asymptomatic, asymptomatic, symptomatic, quarantined, hospitalized and recovered or removed individuals. The problem was modeled in terms of highly nonlinear coupled system of classical order ordinary differential equations (ODEs) which was further generalized with the Atangana-Balaeanu (ABC) fractional derivative in Caputo sense with nonlocal kernel. Furthermore, some theoretical analyses have been done such as boundedness, positivity, existence and uniqueness of the considered. Disease-free and endemic equilibrium points were also assessed. The basic reproduction was calculated through next generation technique. Due to high risk of infection, in the present study, we have considered the reported cases from three continents namely Americas, Europe, and south-east Asia. The reported cases were considered between 1st May 2021 and 31st July 2021 and on the basis of this data, the spread of infection is predicted for the next 200 days. The graphical solution of the considered nonlinear fractional model was obtained via numerical scheme by implementing the MATLAB software. Based on the fitted values of parameters, the basic reproduction number ℜ0 for the case of America, Asia and Europe were calculated as ℜ0≈2.92819, ℜ0≈2.87970 and ℜ0≈2.23507 respectively. It is also observed that the spread of infection in America is comparatively high followed by Asia and Europe. Moreover, the effect of fractional parameter is shown on the dynamics of spread of infection among different classes. Additionally, the effect of quarantined and treatment of infected individuals is also shown graphically. From the present analysis it is observed that awareness of being quarantine and proper treatment can reduce the infection rate dramatically and a minimal variation in quarantine and treatment rates of infected individuals can lead us to decrease the rate of infection.

Sensitivity Analysis of COVID-19 with Quarantine and Vaccination: A Fractal-Fractional Model

To eradicate most infectious diseases, mathematical modelling of contagious diseases has revealed that a combination of quarantine, vaccination, and cure is frequently required. However, eradicating the disease will remain a difficult task if they aren't provided at the appropriate time and in the right quantity. Control analysis has been shown to be an effective way for discovering the best approaches to preventing the spread of contagious diseases through the development of disease preventive interventions. The method comprises reducing the cost of infection, implementing control measures, or both. In order to gain a better understanding of COVID-19's future dynamics, this study presents a compartmental mathematical model. The problem is modelled as a highly nonlinear coupled system of classical order ODEs, which is then generalised using the Mittag-Leffler kernel's fractal-fractional derivative. The uniqueness of the fractional model under discussion has also been demonstrated. The boundedness and non-negativity of the considered model are also established. The next generation technique is used to examine basic reproduction, anddisease free and endemic equilibrium. We used reported cases from Australia in this investigation due to the high risk of infection. The reported cases are considered between 1st July 2021 and 20th August 2021. On the basis of previous data, the spread of infection is predicted for the next 600 days which is shown through different graphs. The graphical solution of the considered nonlinear model is obtained via numerical scheme by implementing the MATLAB software. Based on the fitted values of parameters, the basic reproduction number R0 is calculated as R0≈1.58276. Furthermore, the impact of fractional and fractal parameter on the disease spread among different classes is demonstrated. In addition, the impact of quarantine and vaccination on infected people is dramatically depicted. It's been argued that public awareness of the quarantine and effective vaccination can drastically reduce infection rates in the population.

A Distinctive Method of Online Interactive Learning in STEM Education

A breakthrough that has occurred in recent years is the emergence of the COVID-19 pandemic. It has affected various sectors of society, including the educational sector. It has prevented students from performing group-oriented hands-on activities and has eventually transformed their active learning environment in schools into virtual passive lectures at home. Therefore, to solve this impedance, we exercised several online STEM programs (five online STEM programs with repetitive cycles) for school students, including 140 students (middle and high school), 16 undergraduate (UG) secondary mentors, and 8 primary STEM professionals. Thus, the study revealed the results of a distinctive interactive online STEM teaching model that has been designed to overcome the virtual classroom’s impediments. The employed teaching model demonstrates an interactive learning environment that ensures students’engagement, retention, and participation, driving them to STEM innovations. Various digital tools, including PowerPoint presentations, videos, online simulations, interactive quizzes, and innovative games were used as teaching aids. Both the synchronous and asynchronous means in a student-centered approach, along with the feedback mechanism, were implemented. Finally, the employed method’s effectiveness was revealed by the maximum student retention and STEM innovation rates, along with the model’s potentiality towards its replicability and sustainability. Thus, the outlook of such initiatives could further be broadened by its sustainability and replicability aspect towards vulnerable student communities such as academically introverted and specially challenged students.

A New Flexible Statistical Model: Simulating and Modeling the Survival Times of COVID-19 Patients in China

The spread of the COVID-19 epidemic, since December 2019, has caused much damage around the world, disturbed every aspect of daily life, and has become a serious health threat. The COVID-19 epidemic impacted nearly 150 countries around the globe between December 2019 and March 2020. Since December 2019, researchers have been trying to develop new suitable statistical models to adequately describe the behavior of this deadly pandemic. In this paper, a flexible statistical model has been proposed that can be used to model the lifetime events associated with this deadly pandemic. The new distribution is derived from the combination of an extended Weibull distribution and a trigonometric strategy referred to as the arcsine-X approach. Hence, the new model may be referred to as the arcsine new flexible extended Weibull model. The proposed model is capable of capturing five different behaviors of the hazard rate function. The model parameters are estimated via the maximum likelihood approach. Furthermore, a Monte Carlo study is conducted to assess the behavior of the estimators. Finally, the applicability of the new model is demonstrated using the data of fifty-three patients taken from a hospital in China.

Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China.

Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.

COVID-19 Inspired a STEM-Based Virtual Learning Model for Middle Schools—A Case Study of Qatar

An unprecedented turn in educational pedagogies due to the COVID-19 pandemic has significantly affected the students’ learning process worldwide. This article describes developing a STEM-based online course during the schools’ closure in the COVID-19 epidemic to combat the virtual science classroom’s limitations that could promise an active STEM learning environment. This learning model of the online STEM-based course successfully developed and exercised on 38 primary–preparatory students helped them to overcome the decline in their learning productivity. Various digital learning resources, including PowerPoint presentations, videos, online simulations, interactive quizzes, and innovative games, were implemented as instructional tools to achieve the respective content objectives. A feedback mechanism methodology was executed to improve online instructional delivery and project learners’ role in a student-centered approach, thereby aiding in the course content’s qualitative assessment. The students’ learning behavior provided concrete insights into the program’s positive outcomes, witnessing minimal student withdrawals and maximum completed assignments. Conclusions had been drawn from the course assessment (by incorporating both synchronous and asynchronous means), student feedback, and SWOT analysis to evaluate the course’s effectiveness.

Practice of Academic Surgical Pathology During the COVID-19 Pandemic.

OBJECTIVES: To determine the impact of the coronavirus disease 2019 (COVID-19) pandemic on our service, pre-, and postgraduate education and discuss the measures taken to ensure continued provision of quality service as well as education during the mandatory lockdown. METHODS: Measures taken to protect staff from infection and minimize virus transmission within the department as well as measures taken to allow smooth provision of quality service and uninterrupted pre- and postgraduate education were analyzed. Data were collected regarding case volumes (histology, cytology, and frozen sections) and case complexity during the lockdown and analyzed. RESULTS: Staggered rota was introduced for all staff. Strict social distancing measures were implemented. Staff was extensively counseled regarding the importance of protective measures. Pre- and postgraduate education, which was temporarily suspended, was quickly resumed using online teaching ensuring continuation of academic activities. The volume of cases decreased during the lockdown but complexity increased even more. CONCLUSIONS: Immediate and effective measures were taken to protect staff from infection and ensure smooth provision of quality services. Measures were quickly taken to ensure resumption of pre- and postgraduate academic activities. The volume of cases decreased but complexity increased. There is fear among faculty and staff regarding the future.

A report on COVID-19 epidemic in Pakistan using SEIR fractional model.

Recently, novel coronavirus is a serious global issue and having a negative impact on the economy of the whole world. Like other countries, it also effected the economy and people of Pakistan. According to the publicly reported data, the first case of novel corona virus in Pakistan was reported on 27th February 2020. The aim of the present study is to describe the mathematical model and dynamics of COVID-19 in Pakistan. To investigate the spread of coronavirus in Pakistan, we develop the SEIR time fractional model with newly, developed fractional operator of Atangana-Baleanu. We present briefly the analysis of the given model and discuss its applications using world health organization (WHO) reported data for Pakistan. We consider the available infection cases from 19th March 2020, till 31st March 2020 and accordingly, various parameters are fitted or estimated. It is worth noting that we have calculated the basic reproduction number [Formula: see text] which shows that virus is spreading rapidly. Furthermore, stability analysis of the model at disease free equilibrium DFE and endemic equilibriums EE is performed to observe the dynamics and transmission of the model. Finally, the AB fractional model is solved numerically. To show the effect of the various embedded parameters like fractional parameter [Formula: see text] on the model, various graphs are plotted. It is worth noting that the base of our investigation, we have predicted the spread of disease for next 200 days.

Emergent Aerosols Generating Procedures in Oral & Maxillofacial Surgery in COVID-19 Pandemic

Introduction: COVID-19 has a great impact on the practice of maxillofacial surgery. To fulfill our professional obligations, we continued the services of department by carrying out emergency procedures most of which include aerosol generating procedures which need special consideration and are in high risk category in this current pandemic. In this study we interrogated the literature and evaluated the procedures, their modification and the protocols followed by our team. Objective: To study and analyze the emergency aerosol generating procedures performed, modifications done according to current situation, duration of procedures and recommended precautionary measures followed. Methods: We performed a retrospective observational study in the Department of Oral & Maxillofacial Surgery, Mayo Hospital, Lahore. The patients who presented in emergency and underwent emergency aerosol generating procedures in the duration of two months and 10 days from 1st April'20-10th June'20 were included in the study. Diagnosis, type of emergency procedures performed, duration of procedure, type of anesthesia, precautionary measures followed according to the recommended guidelines and any modification made in the procedures in the current pandemic was assessed and logged on structured proformas. Results: A total of 542 patients were included in the study. Out of 358 cases of trauma closed reduction was performed in 160 cases, open reduction and internal fixation in 49 cases, 19 incision and drainage, 5 debridements, 76 wound irrigation, 7 tracheostomies, 10 resections along with or without neck dissections, 151 laceration repairs, 3 flap divisions, 32 extractions and 2 resection of the lesions along with tracheostomy. Conclusion: During this pandemic, there is a great chance of airborne transmission of virus during aerosol generating procedures. Best possible treatment and care should be provided to the patient along with ensuring protection of patient and the hospital staff by modifying the procedural techniques and following the recommended safety protocols.

Comparison of COVID-19 Pandemic Dynamics in Asian Countries with Statistical Modeling.

In the current scenario, the outbreak of a pandemic disease COVID-19 is of great interest. A broad statistical analysis of this event is still to come, but it is immediately needed to evaluate the disease dynamics in order to arrange the appropriate quarantine activities, to estimate the required number of places in hospitals, the level of individual protection, the rate of isolation of infected persons, and among others. In this article, we provide a convenient method of data comparison that can be helpful for both the governmental and private organizations. Up to date, facts and figures of the total the confirmed cases, daily confirmed cases, total deaths, and daily deaths that have been reported in the Asian countries are provided. Furthermore, a statistical model is suggested to provide a best description of the COVID-19 total death data in the Asian countries.