scholarly journals Final sizes and durations of new COVID-19 pandemic waves in Poland and Germany predicted by generalized SIR model

2021 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Parameter Identification ◽  
Sir Model ◽  
Risk Of Infection ◽  
Identification Procedure ◽  
Epidemic Dynamics

New waves of the COVID-19 pandemic in Europe, which began in the autumn of 2021, are a matter of great concern and the need to immediately predict the epidemic dynamics in order to assess the possible maximum values of new cases, the risk of infection and the number of deaths. The generalized SIR-model and corresponding parameter identification procedure was used to simulate and predict the dynamics of new epidemic waves in Poland and Germany. Results of calculations show that new cases in these countries will not stop to appear in 2022.

scholarly journals Final sizes and durations of new COVID-19 pandemic waves in Ukraine and around the world predicted by generalized SIR model

2021 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Parameter Identification ◽  
Exponential Growth ◽  
Sir Model ◽  
Risk Of Infection ◽  
Identification Procedure ◽  
Epidemic Dynamics ◽  
The World

New waves of the COVID-19 pandemic in Ukraine, which began in the summer of 2021, and after holidays in the middle of October 2021, were characterized by almost exponential growth of smoothed daily numbers of new cases. This is a matter of great concern and the need to immediately predict the epidemic dynamics in order to assess the possible maximum values of new cases, the risk of infection and the number of deaths. The generalized SIR-model and corresponding parameter identification procedure was used to simulate and predict the dynamics of two new epidemic waves in Ukraine and one in the whole world. Results of calculations show that new cases in Ukraine will not stop to appear before November 2022. If the global situation with vaccination, testing and treatment will not change, the pandemic could continue for another ten years.

scholarly journals Simulation and predictions of a new COVID-19 pandemic wave in Ukraine with the use of generalized SIR model

2021 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Parameter Identification ◽  
Exponential Growth ◽  
Sir Model ◽  
New Wave ◽  
Risk Of Infection ◽  
Identification Procedure ◽  
Pandemic Wave ◽  
Epidemic Wave

A new wave of the COVID-19 pandemic in Ukraine, which began in the summer of 2021, was characterized by almost exponential growth of smoothed daily numbers of new cases. This is a matter of great concern and the need to immediately predict the dynamics of the epidemic in order to assess the possible maximum values of new cases, the risk of infection and the number of deaths. The generalized SIR-model and corresponding parameter identification procedure was used to simulate and predict the dynamics of this epidemic wave. The new COVID-19 epidemic wave in Ukraine will begin to subside in mid-October 2021, but its duration will be quite long. Unfortunately, new cases may appear by the summer of 2022.

scholarly journals Will a natural collective immunity of Ukrainians restrain new COVID-19 waves?

2021 ◽  
Author(s):  
Igor Nesteruk
Keyword(s):  
Mathematical Modeling ◽  
Parameter Identification ◽  
Real Number ◽  
Sir Model ◽  
Natural Immunity ◽  
Identification Procedure ◽  
Optimal Value ◽  
The Difference

The visible and real sizes the COVID-19 epidemic in Ukraine were estimated with the use of the number of laboratory-confirmed cases (accumulated in May and June 2021), the generalized SIR-model and the parameter identification procedure taking into account the difference between registered and real number of cases. The calculated optimal value of the visibility coefficient shows that most Ukrainians have already been infected with the coronavirus, and some more than once, i.e., Ukrainians have probably achieved a natural collective immunity. Nevertheless, a large number of new strains and short-lived antibodies can cause new pandemic waves. In particular, the beginning of such a wave, we probably see in Ukraine in mid-July 2021. The further dynamics of the epidemic and its comparison with the results of mathematical modeling will be able to answer many important questions about the natural immunity and effectiveness of vaccines.

Evolution of SARS-CoV-2 in the state of Alagoas-Brazil via an adaptive SIR model

2020 ◽  
pp. 2150040
Author(s):  
I. F. F. Dos Santos ◽  
G. M. A. Almeida ◽  
F. A. B. F. De Moura
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Sir Model ◽  
The State ◽  
Epidemic Dynamics ◽  
Historical Series ◽  
Input Parameters ◽  
Number Formula ◽  
Near Future ◽  
The Basic Reproduction Number

We investigate the spreading of SARS-CoV-2 in the state of Alagoas, northeast of Brazil, via an adaptive susceptible-infected-removed (SIR) model featuring dynamic recuperation and propagation rates. Input parameters are defined based on data made available by Alagoas Secretary of Health from April 19, 2020 on. We provide with the evolution of the basic reproduction number [Formula: see text] and reproduce the historical series of the number of confirmed cases with less than [Formula: see text] error. We offer predictions, from November 16 forward, over the epidemic situation in the near future and show that it will keep decelerating. Furthermore, the same model can be used to study the epidemic dynamics in other countries with great easiness and accuracy.

Experimental parameter identification of flexible joint robot manipulators

Robotica ◽  
2017 ◽  
Vol 36 (3) ◽  
pp. 313-332 ◽  
Author(s):  
Roger Miranda-Colorado ◽  
Javier Moreno-Valenzuela
Keyword(s):  
Parameter Identification ◽  
Numerical Simulations ◽  
Least Squares ◽  
Robot Manipulators ◽  
Experimental Parameter ◽  
Velocity Estimation ◽  
Identification Procedure ◽  
Flexible Joint ◽  
Identification Method ◽  
Estimated Parameters

SUMMARYThis paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

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