Prediction of the Chances of Death in Covid-19 Data Using the Poisson Process

Covid-19 has brought about major changes for all people in various countries, for example creating vaccines, wearing masks and predicting the predictive state of death that will occur. In this paper, we will predict cases of covid-19 deaths using data taken from the worldometer website by taking data on daily covid-19 deaths worldwide in the period January 23rd- April 16th, 2020. Then the data is processed using the Poisson process that has been transformed using SPSS computer programming, namely the daily mortality rate in the period January 23rd - March 16th, 2020 using descriptive statistics, it was found that the death rate was 4 people in one day, then the Kolmogorov test followed the Poisson distribution, because it met the requirements for the P-value. value . Furthermore, it is calculated by using the death process, which is the chance of an event with the chance of death of all the corona suspects in the next 5 days, namely April 21 because the data has been transformed, so . the chance that no one will die within the next 45 days, namely April 30th, 2020 is close to. In the period of January 23rd - April 16th, 2020 using descriptive statistics, it was found that the death rate was 6 people in one day, then the Kolmogorov test was carried out with the results following the Poisson distribution, because it fulfilled the requirements for a P-value . Furthermore, it is calculated using the death process, which is the chance of an event with the chance of death of all the corona suspects in the next 5 days, namely April 21st, 2020 because the data has been transformed, so . The chance that no one will die within the next 45 days, namely May 31, is close to .

Robustness Analysis of Novel ε-Support Vector Regression

The key to the robust ε-support vector regression algorithm is searching for the optimal regression hyper plane while data with disturbance in the X-direction. In the paper, the optimal regression hyper plane and the optimal separating hyper plane are compared and analyzed. By means of Kolmogorov test, it is can be deduced that the testing errors of the robust ε-support vector regression experiments follow normal distribution. The result demonstrates that the algorithm has good forecast accuracy and high robustness.