Hybridized wavelet neuronal learning-based modelling to predict novel COVID-19 effects in India and USA.

Coronavirus disease so called as COVID-19 is an infectious disease and its spread takes place due to human interaction by their pathogen materials during coughing and sneezing. COVID-19 is basically a respiratory disease as evidence proved that a large number of infected people died due to short breathing. Most widely and uncontrollably spreading unknown viral genome infecting people worldwide was announced to be 2019-2020 nCoV by WHO on January 30, 2020. Based on the seriousness of its spread and unavailability of vaccination or any form of treatment, it was an immediate health-emergency of concern of international-level. The paper analyses effects of this virus in countries, such as India and United States on day-to-day basis because of their greater variability. In this study, various performance measures, such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination ( R 2 ) , mean absolute standard error (MASE) and mean absolute percentage error (MAPE) which characterize models' performances. R 2 value has been achieved to be closest to 1, i.e., 0.999 from Wavelet Neuronal Network Fuzzified Inferences' Layered Multivariate Adaptive Regression Spline (WNNFIL-MARS) for both the countries' data. It is important to capture the essence of this pandemic affecting millions of the population daily ever since its spread began from January, 2020.

Machine learned hybrid Gaussian analysis of COVID-19 pandemic in India.

This article discusses short term forecasting of the Novel Corona Virus (COVID -19) data for infected, recovered and active cases using the Machine learned hybrid Gaussian and ARIMA method for the spread in India. The Covid-19 data is obtained from the World meter and MOH (Ministry of Health, India). The data is analyzed for the period from January 30, 2020 (the first case reported) to October 15, 2020. Using ARIMA (2, 1, 0), we obtain the short forecast up to October 31, 2020. The several statistics parameters have tested for the goodness of fit to evaluate the forecasting methods but the results show that ARIMA (2, 1, 0) gives better forecast for the data system. It is observed that COVID 19 data follows quadratic behavior and in long run it spreads with high peak roughly estimated in September 18, 2020. Also, using nonlinear regression it is observed that the trend in long run follows the Gaussian mixture model. It is concluded that COVID 19 will follow secondary shock wave in the month of November 2020. In India we are approaching towards herd immunity. Also, it is observed that the impact of pandemic will be about 441 to 465 days and the pandemic will end in between April-May 2021. It is concluded that primary peak observed in September 2020 and the secondary shock wave to be around November 2020 with sharp peak. Thus, it is concluded that the people should follow precautionary measures and it is better to maintain social distancing with all safety measures as the pandemic situation is not in control due to non-availability of medicines.

Pre-COVID conventional offline teaching v/s intra-COVID online teaching: A descriptive map of preference patterns among first year M.B.B.S students

Introduction: Medical education today is equipped with an armamentarium of newer online-based methods of correspondence courses, computerized virtual patient simulation, many open online courses in medical sciences, and tele-learning. The sudden, unplanned change from conventional teaching to online teaching during COVID-19 poses unique challenges and opportunities for teachers and learners, both. Many themes and principles have emerged for good online teaching learning and assessment practices (GOTLAP). Materials and Methods: The present study, involving 392 MBBS first year students from two universities, was conducted with an aim of comparing students' perception regarding online and offline teaching methodology, and online v/s offline method of assessment and to recommend the principles of GOTLAP. Data collected were analyzed by Strength, Weakness, Opportunity, and Threat (SWOT) analysis to provide a focused measure on how students perceive the program of online teaching and assessment. Results: In the present study, majority of the students (approximately 49.6%) have shown preference for offline teaching methodology, 22.9% has shown similar preference for both methods, while 27.5% has shown preference for the offline teaching method. SWOT analysis applied on qualitative data is a useful tool for assessing our present status in online learning and laying a ground work for formulating GOTLAP and a plan of future strategy. Conclusions: The GOTLAP principles can effectively pave way for the incorporation of blended learning (currently underutilized) in undergraduate medical education.

Nonlinear dynamics for the spread of pathogenesis of COVID-19 pandemic.

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year-2012 MERS plus, most recently, 2019-nCoV eruptions. SARS-CoV &MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people. Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected (y-z), as exposed ones (y) change from 0 to 100 the value of asymptomatically infected (z) increased upto around 58, at exposed ones (y)=100, asymptomatically infected (z) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones (y) changes values upto 180, the value of asymptomatically infected (z) decreases to 25 so for exposed ones (y) from 100 to 180, asymptomatically infected (z) varies from 58 to 25 to 10 follows bifurcation. Also, phase structures of exposed-symptomatically infected (y-u), exposed-removed (y-v), exposed-virus in the reservoir (y-w), asymptomatically infected-removed (z-v), symptomatically infected-removed (u-v) specifically depict bifurcations in various forms at different points. In case of asymptomatically infected-virus in the reservoir (z-w), at asymptomatically infected (z)=10, the value of viruses in the reservoir (w)=50, then as asymptomatically infected (z) increases to upto around 60. At this point, removed ones (v) increase from 50 to 70 and asymptomatically infected (z) decrease to 20 i.e., crosses the same value twice, which shows its limiting is known as limit cycle behavior and both the values tend to decrease towards zero. It shows a closed-loop limit cycle. Today, there has been no scientific revolution in the development of vaccination, nor has any antiviral treatment been successful, resulting in lack of its medication. Based on the phases, time series, and complexity analysis of the model's various parameters, it is studied to understand the variation in this pandemic's scenario.

Mesenchymal stem cell immunomodulation and regeneration therapeutics as an ameliorative approach for COVID-19 pandemics.

The severe acute respiratory syndrome-novel coronavirus mediated COVID-19 has been recently declared a pandemic by the World Health Organization. The primary target of the SARS-CoV-2 virus is the human lungs governed by the ACE-2 receptor of epithelial type II cells/endothelial cells, which promote modulation of the immune response of host cells through generating cytokine storm, inflammation, severe pneumonia symptoms, and secondary complications such as acute respiratory distress syndrome. Although numerous antiviral and anti-parasitic drugs are under clinical trials to combat this pandemic, to date, neither a specific treatment nor any successful vaccine has been established, urging researchers to identify any potential candidate for combating the disease. Mesenchymal stem cells own self-renewal, differentiation, homing, immunomodulation and remains unaffected by the coronavirus on the virtue of the absence of ACE-2 receptors, indicating that MSC's could be used an ameliorative approach for COVID-19. MSCs have shown to combat the disease via various pathways such as repairing the lung epithelial and endothelial cells, reducing hyperimmune response, maintaining the renin-angiotensin system. Although MSCs-based treatment approaches for COVID-19 is still under consideration with limited data, many human clinical trials of MSC's has been initiated to explore their potential for COVID 19 treatment. The current review summarizes and emphasizes on how MSC's modulate the immune response, can repair the lungs from the impact of the virus, and various aspects of MSC's as a remedial source for COVID-19, to provide better insight for biomedical researchers and for those who are fascinated by stem cells as a therapeutic approach.

Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques.

Coronavirus genomic infection-2019 (COVID-19) has been announced as a serious health emergency arising international awareness due to its spread to 201 countries at present. In the month of April of the year 2020, it has certainly taken the pandemic outbreak of approximately 11,16,643 infections confirmed leading to around 59,170 deaths have been recorded world-over. This article studies multiple countries-based pandemic spread for the development of the COVID-19 originated in the China. This paper focuses on forecasting via real-time responses data to inherit an idea about the increase and maximum number of virus-infected cases for the various regions. In addition, it will help to understand the panic that surrounds this nCoV-19 for some intensely affecting states possessing different important demographic characteristics that would be affecting the disease characteristics. This study aims at developing soft-computing hybrid models for calculating the transmissibility of this genome viral. The analysis aids the study of the outbreak of this virus towards the other parts of the continent and the world. A hybrid of wavelet decomposed data into approximations and details then trained & tested through neuronal-fuzzification approach. Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. While data-based prediction through interpolation applied through moving average predicts for longer time spans such as 50-60 days ahead with lesser accuracy as compared to that of wavelet-based hybrids. Based on the simulations, the significance level (alpha) ranges from 0.10 to 0.67, MASE varying from 0.06 to 5.76, sMAPE ranges from 0.15 to 1.97, MAE varies from 22.59 to 6024.76, RMSE shows a variation from 3.18 to 8360.29 & R2 varying through 0.0018 to 0.7149. MASE and sMAPE are relatively lesser applied and novel measures that aimed to achieve increase in accuracy. They eliminated skewness and made the model outlier-free. Estimates of the awaited outburst for regions in this study are India, China and the USA that will help in the improvement of apportionment of healthcare facilities as it can act as an early-warning system for government policy-makers. Thus, data-driven analysis will provide deep insights into the study of transmission of this viral genome estimation towards immensely affected countries. Also, the study with the help of transmission concern aims to eradicate the panic and stigma that has spread like wildfire and has become a significant part of this pandemic in these times.