SEIHCRD Model for COVID-19 Spread Scenarios,Disease Predictions and Estimates the Basic Reproduction Number,Case Fatality Rate,Hospital,and ICU Beds Requirement

We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United States.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.

Study of Non-Pharmacological Interventions on COVID-19 Spread

COVID-19 disease has emerged as one of the life threatening threat to the society.A novel beta coronavirus causes it.It began as unidentified pneumonia of unknown etiology in Wuhan City,Hubei province in China emerged in December 2019.No vaccine has been produced till now.Mathematical models are used to study the impact of different measures used to decrease pandemic.Mathematical models have been designed to estimate the numbers of spreaders in different scenarios in the present manuscript.In the present manuscript,three different mathematical models have been proposed with different scenarios,such as screening,quarantine,andNPIs,to estimate the number of virus spreaders.The analysis shows that the numbers of COVID-19 patients will be more without screening the peoples coming from other countries.Since every people suffering fromCOVID-19 disease are spreaders.The screening and quarantine with NPIs have been implemented to study their impact on the spreaders.It has been found that NPI measures can reduce the number of spreaders.The NPI measures reduce the spread function’s growth and provide decision makers more time to prepare with in dealing with the disease.

A Novel Approach for Enhancement of Geometric and Contrast Resolution Properties of Low Contrast Images

The present work encompasses a new image enhancement algorithm using newly constructed Chebyshev fractional order differentiator. We have used Chebyshev polynomials to design Chebyshev fractional order differentiator. We have generated the high pass filter corresponding to it. The designed filters are applied for decomposing the input image into four bands and low-low(L-L) sub-band is updated using correction coefficients. Reconstructed image with updated L-L sub-band provides the enhanced image. The visual results obtained are encouraging for image enhancement. The applicability of the developed algorithm is illustrated on three different test images.The effects of order of differentiation on the edges of images have also been presented and discussed.