scholarly journals COVID-19: data-driven dynamics, statistical and distributed delay models, and observations

2020 ◽  
Vol 101 (3) ◽  
pp. 1527-1543 ◽  
Author(s):  
Xianbo Liu ◽  
Xie Zheng ◽  
Balakumar Balachandran
Keyword(s):  
Distributed Delay ◽  
Data Driven ◽  
Delay Models

Comments on the paper: Liu X., Zheng X., and Balachandran B., COVID-19: Data-driven dynamics, statistical and distributed delay models, and observations, Nonlinear Dynamics, 2020. //doi.org/10.1007/s11071-020-05863-5.

2020 ◽  
pp. 1-1
Author(s):  
Gabor Stepan
Keyword(s):  
Nonlinear Dynamics ◽  
Distributed Delay ◽  
Data Driven ◽  
Global Community ◽  
University Of Maryland ◽  
Maryland College ◽  
Delay Models ◽  
Global Spread ◽  
College Park ◽  
The University

Abstract It is a welcome surprise when mechanicians and mechanical engineers have relevant and important comments on the global spread of the COVID-19 virus. The above paper of Professor Bala Balachandran of the University of Maryland, College Park, MD and his group represents just one of these relevant comments to the global community.

DISTRIBUTED-DELAY MODELS OF THE GLUCOSE-INSULIN HOMEOSTASIS AND ASYMPTOTIC STATE OBSERVATION

2005 ◽  
Vol 38 (1) ◽  
pp. 1041-1046 ◽  
Author(s):  
A. De Gaetano ◽  
D. Di Martino ◽  
A. Germani ◽  
C. Manes ◽  
P. Palumbo
Keyword(s):  
Distributed Delay ◽  
Asymptotic State ◽  
State Observation ◽  
Insulin Homeostasis ◽  
Delay Models

scholarly journals Stability switches in distributed delay models

1985 ◽  
Vol 109 (2) ◽  
pp. 388-396 ◽  
Author(s):  
S.P Blythe ◽  
R.M Nisbet ◽  
W.S.C Gurney ◽  
N MacDonald
Keyword(s):  
Distributed Delay ◽  
Stability Switches ◽  
Delay Models

Attrition in Distributed Delay Models

1977 ◽  
Vol 7 (9) ◽  
pp. 635-638 ◽  
Author(s):  
John Vansickle
Keyword(s):  
Distributed Delay ◽  
Delay Models

scholarly journals Application of Distribution-Delay Models to Estimating the Hospitalized Mortality Rate of Covid-19 According to Delay Effect of Hospitalizations Counts

2021 ◽  
Author(s):  
Sedighe Rastaghi ◽  
Noushin Akbari Shark ◽  
Azadeh Saki
Keyword(s):  
Mortality Rate ◽  
Measurement Errors ◽  
Distributed Delay ◽  
Delay Model ◽  
Delay Effect ◽  
Global Rate ◽  
Long Run ◽  
The World ◽  
Delay Models ◽  
Daily Hospitalization

Introduction:The COVID-19 infectious epidemic has become a serious worry all over the world, including Iran. The high outbreak of disease ranked Iran as second in Asia and 11th in the world. Given the growing progress of this epidemic in infecting and killing individuals, it is essential to forecast the delay effect of the number of hospitalized upon the hospitalized mortality rate. Methods: In this study, we used the daily Hospitalization cases of COVID-19 of IRAN for the period of 15-May 2020 to 5-Oct 2020 which were obtained from the online database. Five distribution delay models were compared for estimating and forecasting. Results: Based on measurement errors DDM selected as the best model for forecasting the number of death. According to this model, the long-run effects show that observing the effect of hospitalization counts on death counts takes an average of five days and the long-run hospitalized mortality rate was 12%. Conclusion: The overall hospitalized mortality rate of COVID-19 in Iran is less than the global rate of 15%. The mean of delay effect of daily hospitalization on mortality is approximately 5 days. Our findings showed distributed delay model (DDM) has better performance in the forecasting of the future behavior of the Coronavirus mortality, and providing to government and health care decision- makers the possibility to predict the outcomes of their decision on public health.

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