scholarly journals Density function analysis for a stochastic SEIS epidemic model with non-degenerate diffusion

2017 ◽  
Vol 22 (11) ◽  
pp. 0-0
Author(s):  
Qun Liu ◽  
◽  
Qingmei Chen ◽  
Keyword(s):  
Density Function ◽  
Epidemic Model ◽  
Function Analysis ◽  
Degenerate Diffusion

Characterization of pressure fluctuations from a gas–solid fluidized bed by structure density function analysis

2015 ◽  
Vol 129 ◽  
pp. 156-167 ◽  
Author(s):  
Yumin Chen ◽  
C. Jim Lim ◽  
John R. Grace ◽  
Junying Zhang ◽  
Yongchun Zhao ◽  
...  
Keyword(s):  
Density Function ◽  
Fluidized Bed ◽  
Function Analysis ◽  
Pressure Fluctuations ◽  
Structure Density

scholarly journals A Phenomenological Epidemic Model Based On the Spatio-Temporal Evolution of a Gaussian Probability Density Function

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2000
Author(s):  
Domingo Benítez ◽  
Gustavo Montero ◽  
Eduardo Rodríguez ◽  
David Greiner ◽  
Albert Oliver ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Epidemic Model ◽  
Temporal Evolution ◽  
Growth Models ◽  
Model Fit ◽  
Gaussian Probability Density Function ◽  
Spatio Temporal

A novel phenomenological epidemic model is proposed to characterize the state of infectious diseases and predict their behaviors. This model is given by a new stochastic partial differential equation that is derived from foundations of statistical physics. The analytical solution of this equation describes the spatio-temporal evolution of a Gaussian probability density function. Our proposal can be applied to several epidemic variables such as infected, deaths, or admitted-to-the-Intensive Care Unit (ICU). To measure model performance, we quantify the error of the model fit to real time-series datasets and generate forecasts for all the phases of the COVID-19, Ebola, and Zika epidemics. All parameters and model uncertainties are numerically quantified. The new model is compared with other phenomenological models such as Logistic Grow, Original, and Generalized Richards Growth models. When the models are used to describe epidemic trajectories that register infected individuals, this comparison shows that the median RMSE error and standard deviation of the residuals of the new model fit to the data are lower than the best of these growing models by, on average, 19.6% and 35.7%, respectively. Using three forecasting experiments for the COVID-19 outbreak, the median RMSE error and standard deviation of residuals are improved by the performance of our model, on average by 31.0% and 27.9%, respectively, concerning the best performance of the growth models.

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